Cell-free dna for assessing and/or treating cancer

ABSTRACT

This document relates to methods and materials for assessed, monitored, and/or treated mammals (e.g., humans) having cancer. For example, methods and materials for identifying a mammal as having cancer (e.g., a localized cancer) are provided. For example, methods and materials for assessing, monitoring, and/or treating a mammal having cancer are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Application Ser. No. 62/673,516, filed on May 18, 2018, and claims the benefit of U.S. Patent Application Ser. No. 62/795,900, filed on Jan. 23, 2019. The disclosure of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application.

STATEMENT REGARDING FEDERAL FUNDING

This invention was made with U.S. government support under grant No. CA121113 from the National Institutes of Health. The U.S. government has certain rights in the invention.

BACKGROUND I. Technical Field

This document relates to methods and materials for assessing and/or treating mammals (e.g., humans) having cancer. For example, this document provides methods and materials for identifying a mammal as having cancer (e.g., a localized cancer). For example, this document provides methods and materials for monitoring and/or treating a mammal having cancer.

2. BACKGROUND INFORMATION

Much of the morbidity and mortality of human cancers world-wide is a result of the late diagnosis of these diseases, where treatments are less effective (Torre et al., 2015 CA Cancer J Clin 65:87; and World Health Organization, 2017 Guide to Cancer Early Diagnosis). Unfortunately, clinically proven biomarkers that can be used to broadly diagnose and treat patients are not widely available (Mazzucchelli, 2000 Advances in clinical pathology 4:111; Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung & circulation 20:634; Lin et al., 2016 in Screening for Colorectal Cancer: A Systematic Review for the U.S. Preventive Services Task Force. (Rockville, Md.); Wanebo et al., 1978 N Engl J Med 299:448; and Zauber, 2015 Dig Dis Sci 60:681).

SUMMARY

Recent analyses of cell-free DNA suggests that such approaches may provide new avenues for early diagnosis (Phallen et al., 2017 Sci Trans' Med 9; Cohen et al., 2018 Science 359:926; Alix-Panabieres et al., 2016 Cancer discovery 6:479; Siravegna et al., 2017 Nature reviews. Clinical oncology 14:531; Haber et al., 2014 Cancer discovery 4:650; Husain et al., 2017 JAMA 318:1272; and Wan et al., 2017 Nat Rev Cancer 17:223).

This document provides methods and materials for determining a cell free DNA (cfDNA) fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. This document also provides methods and materials for assessing and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile. In some cases, this document provides methods and materials for monitoring and/or treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on a cfDNA fragmentation profile) to treat the mammal.

Described herein is a non-invasive method for the early detection and localization of cancer. cfDNA in the blood can provide a non-invasive diagnostic avenue for patients with cancer. As demonstrated herein, DNA Evaluation of Fragments for early Interception (DELFI) was developed and used to evaluate genome-wide fragmentation patterns of cfDNA of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers as well as 245 healthy individuals. These analyses revealed that cfDNA profiles of healthy individuals reflected nucleosomal fragmentation patterns of white blood cells, while patients with cancer had altered fragmentation profiles. DELFI had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity and identified the tissue of origin of the cancers to a limited number of sites in 75% of cases. Assessing cfDNA (e.g., using DELFI) can provide a screening approach for early detection of cancer, which can increase the chance for successful treatment of a patient having cancer. Assessing cfDNA (e.g., using DELFI) can also provide an approach for monitoring cancer, which can increase the chance for successful treatment and improved outcome of a patient having cancer. In addition, a cfDNA fragmentation profile can be obtained from limited amounts of cfDNA and using inexpensive reagents and/or instruments.

In general, one aspect of this document features methods for determining a cfDNA fragmentation profile of a mammal. The methods can include, or consist essentially of, processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries, subjecting the sequencing libraries to whole genome sequencing (e.g., low-coverage whole genome sequencing) to obtain sequenced fragments, mapping the sequenced fragments to a genome to obtain windows of mapped sequences, and analyzing the windows of mapped sequences to determine cfDNA fragment lengths. The mapped sequences can include tens to thousands of windows. The windows of mapped sequences can be non-overlapping windows. The windows of mapped sequences can each include about 5 million base pairs. The cfDNA fragmentation profile can be determined within each window. The cfDNA fragmentation profile can include a median fragment size. The cfDNA fragmentation profile can include a fragment size distribution. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences. The cfDNA fragmentation profile can be over the whole genome. The cfDNA fragmentation profile can be over a subgenomic interval (e.g., an interval in a portion of a chromosome).

In another aspect, this document features methods for identifying a mammal as having cancer. The methods can include, or consist essentially of, determining a cfDNA fragmentation profile in a sample obtained from a mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile in the sample obtained from the mammal is different from the reference cfDNA fragmentation profile. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from the healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a median fragment size, and a median fragment size of the cfDNA fragmentation profile can be shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a fragment size distribution, and a fragment size distribution of the cfDNA fragmentation profile can differ by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include position dependent differences in fragmentation patterns, including a ratio of small cfDNA fragments to large cfDNA fragments, where a small cfDNA fragment can be 100 base pairs (bp) to 150 bp in length and a large cfDNA fragments can be 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile can be lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include sequence coverage of small cfDNA fragments, large cfDNA fragments, or of both small and large cfDNA fragments, across the genome. The cancer can be colorectal cancer, lung cancer, breast cancer, bile duct cancer, pancreatic cancer, gastric cancer, or ovarian cancer. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile in windows across the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval (e.g., an interval in a portion of a chromosome). The mammal can have been previously administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof. The method also can include administering to the mammal a cancer treatment (e.g., surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof). The mammal can be monitored for the presence of cancer after administration of the cancer treatment.

In another aspect, this document features methods for treating a mammal having cancer. The methods can include, or consist essentially of, identifying the mammal as having cancer, where the identifying includes determining a cfDNA fragmentation profile in a sample obtained from the mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile obtained from the mammal is different from the reference cfDNA fragmentation profile; and administering a cancer treatment to the mammal. The mammal can be a human. The cancer can be colorectal cancer, lung cancer, breast cancer, gastric cancers, pancreatic cancers, bile duct cancers, or ovarian cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from a healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profile can include a median fragment size, where a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a fragment size distribution, where a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences, where a small cfDNA fragment is 100 bp to 150 bp in length, where a large cfDNA fragments is 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile is lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include the sequence coverage of small cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of large cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of small and large cfDNA fragments in windows across the genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval. The mammal can have previously been administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The method also can include monitoring the mammal for the presence of cancer after administration of the cancer treatment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1. Schematic of an exemplary DELFI approach. Blood is collected from a cohort of healthy individuals and patients with cancer. Nucleosome protected cfDNA is extracted from the plasma fraction, processed into sequencing libraries, examined through whole genome sequencing, mapped to the genome, and analyzed to determine cfDNA fragment profiles in different windows across the genome. Machine learning approaches are used to categorize individuals as healthy or as having cancer and to identify the tumor tissue of origin using genome-wide cfDNA fragmentation patterns.

FIG. 2. Simulations of non-invasive cancer detection based on number of alterations analyzed and tumor-derived cfDNA fragment distributions. Monte Carlo simulations were performed using different numbers of tumor-specific alterations to evaluate the probability of detecting cancer alterations in cfDNA at the indicated fraction of tumor-derived molecules. The simulations were performed assuming an average of 2000 genome equivalents of cfDNA and the requirement of five or more observations of any alteration. These analyses indicate that increasing the number of tumor-specific alterations improves the sensitivity of detection of circulating tumor DNA.

FIG. 3. Tumor-derived cfDNA fragment distributions. Cumulative density functions of cfDNA fragment lengths of 42 loci containing tumor-specific alterations from 30 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands (blue). Lengths of mutant cfDNA fragments were significantly different in size compared to wild-type cfDNA fragments (red) at these loci.

FIGS. 4A and 4B. Tumor-derived cfDNA GC content and fragment length. A, GC content was similar for mutated and non-mutated fragments. B, GC content was not correlated to fragment length.

FIG. 5. Germline cfDNA fragment distributions. Cumulative density functions of fragment lengths of 44 loci containing germline alterations (non-tumor derived) from 38 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. Fragments with germline mutations (blue) were comparable in length to wild-type cfDNA fragment lengths (red).

FIG. 6. Hematopoietic cfDNA fragment distributions. Cumulative density functions of fragment lengths of 41 loci containing hematopoietic alterations (non-tumor derived) from 28 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. After correction for multiple testing, there were no significant differences (α=0.05) in the size distributions of mutated hematopoietic cfDNA fragments (blue) and wild-type cfDNA fragments (red).

FIGS. 7A-7F. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Genome-wide cfDNA fragmentation profiles (defined as the ratio of short to long fragments) from ˜9× whole genome sequencing are shown in 5 Mb bins for 30 healthy individuals (top) and 8 lung cancer patients (bottom). B, An analysis of healthy cfDNA (top), lung cancer cfDNA (middle), and healthy lymphocyte (bottom) fragmentation profiles and lymphocyte profiles from chromosome 1 at 1 Mb resolution. The healthy lymphocyte profiles were scaled with a standard deviation equal to that of the median healthy cfDNA profiles. Healthy cfDNA patterns closely mirrored those in healthy lymphocytes while lung cancer cfDNA profiles were more varied and differed from both healthy and lymphocyte profiles. C, Smoothed median distances between adjacent nucleosome centered at zero using 100 kb bins from healthy cfDNA (top) and nuclease-digested healthy lymphocytes (middle) are depicted together with the first eigenvector for the genome contact matrix obtained through previously reported Hi-C analyses of lymphoblastoid cells (bottom). Healthy cfDNA nucleosome distances closely mirrored those in nuclease-digested lymphocytes as well as those from lymphoblastoid Hi-C analyses. cfDNA fragmentation profiles from healthy individuals (n=30) had high correlations while patients with lung cancer had lower correlations to median fragmentation profiles of lymphocytes (D), healthy cfDNA (E), and lymphocyte nucleosome (F) distances.

FIG. 8. Density of cfDNA fragment lengths in healthy individuals and patients with lung cancer. cfDNA fragments lengths are shown for healthy individuals (n=30, gray) and patients with lung cancer (n=8, blue).

FIGS. 9A and 9B. Subsampling of whole genome sequence data for analysis of cfDNA fragmentation profiles. A, High coverage (9×) whole-genome sequencing data were subsampled to 2×, 1×, 0.5×, 0.2×, and 0.1× fold coverage. Mean centered genome-wide fragmentation profiles in 5 Mb bins for 30 healthy individuals and 8 patients with lung cancer are depicted for each subsampled fold coverage with median profiles shown in blue. B, Pearson correlation of subsampled profiles to initial profile at 9× coverage for healthy individuals and patients with lung cancer.

FIG. 10. cfDNA fragmentation profiles and sequence alterations during therapy. Detection and monitoring of cancer in serial blood draws from NSCLC patients (n=19) undergoing treatment with targeted tyrosine kinase inhibitors (black arrows) was performed using targeted sequencing (top) and genome-wide fragmentation profiles (bottom). For each case, the vertical axis of the lower panel displays −1 times the correlation of each sample to the median healthy cfDNA fragmentation profile. Error bars depict confidence intervals from binomial tests for mutant allele fractions and confidence intervals calculated using Fisher transformation for genome-wide fragmentation profiles. Although the approaches analyze different aspects of cfDNA (whole genome compared to specific alterations) the targeted sequencing and fragmentation profiles were similar for patients responding to therapy as well as those with stable or progressive disease. As fragmentation profiles reflect both genomic and epigenomic alterations, while mutant allele fractions only reflect individual mutations, mutant allele fractions alone may not reflect the absolute level of correlation of fragmentation profiles to healthy individuals.

FIGS. 11A-11C. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Fragmentation profiles (bottom) in the context of tumor copy number changes (top) in a colorectal cancer patient where parallel analyses of tumor tissue were performed. The distribution of segment means and integer copy numbers are shown at top right in the indicated colors. Altered fragmentation profiles were present in regions of the genome that were copy neutral and were further affected in regions with copy number changes. B, GC adjusted fragmentation profiles from 1-2× whole genome sequencing for healthy individuals and patients with cancer are depicted per cancer type using 5 Mb windows. The median healthy profile is indicated in black and the 98% confidence band is shown in gray. For patients with cancer, individual profiles are colored based on their correlation to the healthy median. C, Windows are indicated in orange if more than 10% of the cancer samples had a fragment ratio more than three standard deviations from the median healthy fragment ratio. These analyses highlight the multitude of position dependent alterations across the genome in cfDNA of individuals with cancer.

FIGS. 12A and 12B. Profiles of cfDNA fragment lengths in copy neutral regions in healthy individuals and one patient with colorectal cancer. A, The fragmentation profile in 211 copy neutral windows in chromosomes 1-6 for 25 randomly selected healthy individuals (gray). For a patient with colorectal cancer (CGCRC291) with an estimated mutant allele fraction of 20%, the cancer fragment length profile was diluted to an approximate 10% tumor contribution (blue). A and B, While the marginal densities of the fragment profiles for the healthy samples and cancer patient show substantial overlap (A, right), the fragmentation profiles are different as can be seen visualization of the fragmentation profiles (A, left) and by the separation of the colorectal cancer patient from the healthy samples in a principal component analysis (B).

FIGS. 13A and 13B. Genome-wide GC correction of cfDNA fragments. To estimate and control for the effects of GC content on sequencing coverage, coverage in non-overlapping 100 kb genomic windows was calculated across the autosomes. For each window, the average GC of the aligned fragments was calculated. A, Loess smoothing of raw coverage (top row) for two randomly selected healthy subjects (CGPLH189 and CGPLH380) and two cancer patients (CGPLLU161 and CGPLBR24) with undetectable aneuploidy (PA score <2.35). After subtracting the average coverage predicted by the loess model, the residuals were resealed to the median autosomal coverage (bottom row). As fragment length may also result in coverage biases, this GC correction procedure was performed separately for short (<150 bp) and long (>151 bp) fragments. While the 100 kb bins on chromosome 19 (blue points) consistently have less coverage than predicted by the loess model, we did not implement a chromosome-specific correction as such an approach would remove the effects of chromosomal copy number on coverage. B, Overall, a limited correlation was found between short or long fragment coverage and GC content after correction among healthy subjects and cancer patients with a PA score <3.

FIG. 14. Schematic of machine learning model. Gradient tree boosting machine learning was used to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual. The machine learning model included fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. A 10-fold cross validation approach was employed in which each sample is randomly assigned to a fold and 9 of the folds (90% of the data) are used for training and one fold (10% of the data) is used for testing. The prediction accuracy from a single cross validation is an average over the 10 possible combinations of test and training sets. As this prediction accuracy can reflect bias from the initial randomization of patients, the entire procedure was repeat, including the randomization of patients to folds, 10 times. For all cases, feature selection and model estimation were performed on training data and were validated on test data and the test data were never used for feature selection. Ultimately, a DELFI score was obtained that could be used to classify individuals as likely healthy or having cancer.

FIG. 15. Distribution of AUCs across the repeated 10-fold cross-validation. The 25^(th), 50^(th), and 75^(t)h percentiles of the 100 AUCs for the cohort of 215 healthy individuals and 208 patients with cancer are indicated by dashed lines.

FIGS. 16A and 16B. Whole-genome analyses of chromosomal arm copy number changes and mitochondrial genome representation. A, Z scores for each autosome arm are depicted for healthy individuals (n=215) and patients with cancer (n=208). The vertical axis depicts normal copy at zero with positive and negative values indicating arm gains and losses, respectively. Z scores greater than 50 or less than −50 are thresholded at the indicated values. B, The fraction of reads mapping to the mitochondrial genome is depicted for healthy individuals and patients with cancer.

FIGS. 17A and 17B. Detection of cancer using DELFI. A, Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and 208 patients with cancer (DELFI, AUC=0.94), with >95% specificity shaded in blue. Machine learning analyses of chromosomal arm copy number (Chr copy number (ML)), and mitochondrial genome copy number (mtDNA), are shown in the indicated colors. B, Analyses of individual cancers types using the DELFI-combined approach had AUCs ranging from 0.86 to >0.99.

FIG. 18. DELFI detection of cancer by stage. Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and each stage of 208 patients with cancer with >95% specificity shaded in blue.

FIG. 19. DELFI tissue of origin prediction. Receiver operator characteristics for DELFI tissue prediction of bile duct, breast, colorectal, gastric, lung, ovarian, and pancreatic cancers are depicted. In order to increase sample sizes within cancer type classes, cases detected with a 90% specificity were included, and the lung cancer cohort was supplemented with the addition of baseline cfDNA data from 18 lung cancer patients with prior treatment (see, e.g., Shen et al., 2018 Nature, 563:579-583).

FIG. 20. Detection of cancer using DELFI and mutation-based cfDNA approaches. DELFI (green) and targeted sequencing for mutation identification (blue) were performed independently in a cohort of 126 patients with breast, bile duct, colorectal, gastric, lung, or ovarian cancers. The number of individuals detected by each approach and in combination are indicated for DELFI detection with a specificity of 98%, targeted sequencing specificity at >99%, and a combined specificity of 98%. ND indicates not detected.

DETAILED DESCRIPTION

This document provides methods and materials for determining a cfDNA fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). As used herein, the terms “fragmentation profile,” “position dependent differences in fragmentation patterns,” and “differences in fragment size and coverage in a position dependent manner across the genome” are equivalent and can be used interchangeably. In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. As described herein, a cfDNA fragmentation profile of a mammal having cancer is more heterogeneous (e.g., in fragment lengths) than a cfDNA fragmentation profile of a healthy mammal (e.g., a mammal not having cancer). As such, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence and, optionally, the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for monitoring a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and one or more cancer treatments can be administered to the mammal.

A cfDNA fragmentation profile can include one or more cfDNA fragmentation patterns. A cfDNA fragmentation pattern can include any appropriate cfDNA fragmentation pattern. Examples of cfDNA fragmentation patterns include, without limitation, median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, a cfDNA fragmentation pattern includes two or more (e.g., two, three, or four) of median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, cfDNA fragmentation profile can be a genome-wide cfDNA profile (e.g., a genome-wide cfDNA profile in windows across the genome). In some cases, cfDNA fragmentation profile can be a targeted region profile. A targeted region can be any appropriate portion of the genome (e.g., a chromosomal region). Examples of chromosomal regions for which a cfDNA fragmentation profile can be determined as described herein include, without limitation, a portion of a chromosome (e.g., a portion of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and/or 14q) and a chromosomal arm (e.g., a chromosomal arm of 8q, 13q, 11q, and/or 3p). In some cases, a cfDNA fragmentation profile can include two or more targeted region profiles.

In some cases, a cfDNA fragmentation profile can be used to identify changes (e.g., alterations) in cfDNA fragment lengths. An alteration can be a genome-wide alteration or an alteration in one or more targeted regions/loci. A target region can be any region containing one or more cancer-specific alterations. Examples of cancer-specific alterations, and their chromosomal locations, include, without limitation, those shown in Table 3 (Appendix C) and those shown in Table 6 (Appendix F). In some cases, a cfDNA fragmentation profile can be used to identify (e.g., simultaneously identify) from about 10 alterations to about 500 alterations (e.g., from about 25 to about 500, from about 50 to about 500, from about 100 to about 500, from about 200 to about 500, from about 300 to about 500, from about 10 to about 400, from about 10 to about 300, from about 10 to about 200, from about 10 to about 100, from about 10 to about 50, from about 20 to about 400, from about 30 to about 300, from about 40 to about 200, from about 50 to about 100, from about 20 to about 100, from about 25 to about 75, from about 50 to about 250, or from about 100 to about 200, alterations).

In some cases, a cfDNA fragmentation profile can be used to detect tumor-derived DNA. For example, a cfDNA fragmentation profile can be used to detect tumor-derived DNA by comparing a cfDNA fragmentation profile of a mammal having, or suspected of having, cancer to a reference cfDNA fragmentation profile (e.g., a cfDNA fragmentation profile of a healthy mammal and/or a nucleosomal DNA fragmentation profile of healthy cells from the mammal having, or suspected of having, cancer). In some cases, a reference cfDNA fragmentation profile is a previously generated profile from a healthy mammal. For example, methods provided herein can be used to determine a reference cfDNA fragmentation profile in a healthy mammal, and that reference cfDNA fragmentation profile can be stored (e.g., in a computer or other electronic storage medium) for future comparison to a test cfDNA fragmentation profile in mammal having, or suspected of having, cancer. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over the whole genome. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over a subgenomic interval.

In some cases, a cfDNA fragmentation profile can be used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer).

A cfDNA fragmentation profile can include a cfDNA fragment size pattern. cfDNA fragments can be any appropriate size. For example, cfDNA fragment can be from about 50 base pairs (bp) to about 400 bp in length. As described herein, a mammal having cancer can have a cfDNA fragment size pattern that contains a shorter median cfDNA fragment size than the median cfDNA fragment size in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have cfDNA fragment sizes having a median cfDNA fragment size from about 166.6 bp to about 167.2 bp (e.g., about 166.9 bp). In some cases, a mammal having cancer can have cfDNA fragment sizes that are, on average, about 1.28 bp to about 2.49 bp (e.g., about 1.88 bp) shorter than cfDNA fragment sizes in a healthy mammal. For example, a mammal having cancer can have cfDNA fragment sizes having a median cfDNA fragment size of about 164.11 bp to about 165.92 bp (e.g., about 165.02 bp).

A cfDNA fragmentation profile can include a cfDNA fragment size distribution. As described herein, a mammal having cancer can have a cfDNA size distribution that is more variable than a cfDNA fragment size distribution in a healthy mammal. In some case, a size distribution can be within a targeted region. A healthy mammal (e.g., a mammal not having cancer) can have a targeted region cfDNA fragment size distribution of about 1 or less than about 1. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is longer (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp longer, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is shorter (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp shorter, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is about 47 bp smaller to about 30 bp longer than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, a 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20 or more bp difference in lengths of cfDNA fragments. For example, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, about a 13 bp difference in lengths of cfDNA fragments. In some case, a size distribution can be a genome-wide size distribution. A healthy mammal (e.g., a mammal not having cancer) can have very similar distributions of short and long cfDNA fragments genome-wide. In some cases, a mammal having cancer can have, genome-wide, one or more alterations (e.g., increases and decreases) in cfDNA fragment sizes. The one or more alterations can be any appropriate chromosomal region of the genome. For example, an alteration can be in a portion of a chromosome. Examples of portions of chromosomes that can contain one or more alterations in cfDNA fragment sizes include, without limitation, portions of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and 14q. For example, an alteration can be across a chromosome arm (e.g., an entire chromosome arm).

A cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments and a correlation of fragment ratios to reference fragment ratios. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a small cfDNA fragment can be from about 100 bp in length to about 150 bp in length. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a large cfDNA fragment can be from about 151 bp in length to 220 bp in length. As described herein, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is lower (e.g., 2-fold lower, 3-fold lower, 4-fold lower, 5-fold lower, 6-fold lower, 7-fold lower, 8-fold lower, 9-fold lower, 10-fold lower, or more) than in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) of about 1 (e.g., about 0.96). In some cases, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is, on average, about 0.19 to about 0.30 (e.g., about 0.25) lower than a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) in a healthy mammal.

A cfDNA fragmentation profile can include coverage of all fragments. Coverage of all fragments can include windows (e.g., non-overlapping windows) of coverage. In some cases, coverage of all fragments can include windows of small fragments (e.g., fragments from about 100 bp to about 150 bp in length). In some cases, coverage of all fragments can include windows of large fragments (e.g., fragments from about 151 bp to about 220 bp in length).

In some cases, a cfDNA fragmentation profile can be used to identify the tissue of origin of a cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, or an ovarian cancer). For example, a cfDNA fragmentation profile can be used to identify a localized cancer. When a cfDNA fragmentation profile includes a targeted region profile, one or more alterations described herein (e.g., in Table 3 (Appendix C) and/or in Table 6 (Appendix F)) can be used to identify the tissue of origin of a cancer. In some cases, one or more alterations in chromosomal regions can be used to identify the tissue of origin of a cancer.

A cfDNA fragmentation profile can be obtained using any appropriate method. In some cases, cfDNA from a mammal (e.g., a mammal having, or suspected of having, cancer) can be processed into sequencing libraries which can be subjected to whole genome sequencing (e.g., low-coverage whole genome sequencing), mapped to the genome, and analyzed to determine cfDNA fragment lengths. Mapped sequences can be analyzed in non-overlapping windows covering the genome. Windows can be any appropriate size. For example, windows can be from thousands to millions of bases in length. As one non-limiting example, a window can be about 5 megabases (Mb) long. Any appropriate number of windows can be mapped. For example, tens to thousands of windows can be mapped in the genome. For example, hundreds to thousands of windows can be mapped in the genome. A cfDNA fragmentation profile can be determined within each window. In some cases, a cfDNA fragmentation profile can be obtained as described in Example 1. In some cases, a cfDNA fragmentation profile can be obtained as shown in FIG. 1.

In some cases, methods and materials described herein also can include machine learning. For example, machine learning can be used for identifying an altered fragmentation profile (e.g., using coverage of cfDNA fragments, fragment size of cfDNA fragments, coverage of chromosomes, and mtDNA).

In some cases, methods and materials described herein can be the sole method used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). For example, determining a cfDNA fragmentation profile can be the sole method used to identify a mammal as having cancer.

In some cases, methods and materials described herein can be used together with one or more additional methods used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). Examples of methods used to identify a mammal as having cancer include, without limitation, identifying one or more cancer-specific sequence alterations, identifying one or more chromosomal alterations (e.g., aneuploidies and rearrangements), and identifying other cfDNA alterations. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more cancer-specific mutations in a mammal's genome to identify a mammal as having cancer. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more aneuploidies in a mammal's genome to identify a mammal as having cancer.

In some aspects, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying the location (e.g., the anatomic site or tissue of origin) of a cancer in a mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and administering one or more cancer treatments to the mammal. In some cases, this document provides methods and materials for treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal) to treat the mammal. In some cases, during or after the course of a cancer treatment (e.g., any of the cancer treatments described herein), a mammal can undergo monitoring (or be selected for increased monitoring) and/or further diagnostic testing. In some cases, monitoring can include assessing mammals having, or suspected of having, cancer by, for example, assessing a sample (e.g., a blood sample) obtained from the mammal to determine the cfDNA fragmentation profile of the mammal as described herein, and changes in the cfDNA fragmentation profiles over time can be used to identify response to treatment and/or identify the mammal as having cancer (e.g., a residual cancer).

Any appropriate mammal can be assessed, monitored, and/or treated as described herein. A mammal can be a mammal having cancer. A mammal can be a mammal suspected of having cancer. Examples of mammals that can be assessed, monitored, and/or treated as described herein include, without limitation, humans, primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats. For example, a human having, or suspected of having, cancer can be assessed to determine a cfDNA fragmentation profiled as described herein and, optionally, can be treated with one or more cancer treatments as described herein.

Any appropriate sample from a mammal can be assessed as described herein (e.g., assessed for a DNA fragmentation pattern). In some cases, a sample can include DNA (e.g., genomic DNA). In some cases, a sample can include cfDNA (e.g., circulating tumor DNA (cfDNA)). In some cases, a sample can be fluid sample (e.g., a liquid biopsy). Examples of samples that can contain DNA and/or polypeptides include, without limitation, blood (e.g., whole blood, serum, or plasma), amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smears, breast milk, and exhaled breath condensate. For example, a plasma sample can be assessed to determine a cfDNA fragmentation profiled as described herein.

A sample from a mammal to be assessed as described herein (e.g., assessed for a DNA fragmentation pattern) can include any appropriate amount of cfDNA. In some cases, a sample can include a limited amount of DNA. For example, a cfDNA fragmentation profile can be obtained from a sample that includes less DNA than is typically required for other cfDNA analysis methods, such as those described in, for example, Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; and Newman et al., 2016 Nat Biotechnol 34:547).

In some cases, a sample can be processed (e.g., to isolate and/or purify DNA and/or polypeptides from the sample). For example, DNA isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), protein removal (e.g., using a protease), and/or RNA removal (e.g., using an RNase). As another example, polypeptide isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), DNA removal (e.g., using a DNase), and/or RNA removal (e.g., using an RNase).

A mammal having, or suspected of having, any appropriate type of cancer can be assessed (e.g., to determine a cfDNA fragmentation profile) and/or treated (e.g., by administering one or more cancer treatments to the mammal) using the methods and materials described herein. A cancer can be any stage cancer. In some cases, a cancer can be an early stage cancer. In some cases, a cancer can be an asymptomatic cancer. In some cases, a cancer can be a residual disease and/or a recurrence (e.g., after surgical resection and/or after cancer therapy). A cancer can be any type of cancer. Examples of types of cancers that can be assessed, monitored, and/or treated as described herein include, without limitation, colorectal cancers, lung cancers, breast cancers, gastric cancers, pancreatic cancers, bile duct cancers, and ovarian cancers.

When treating a mammal having, or suspected of having, cancer as described herein, the mammal can be administered one or more cancer treatments. A cancer treatment can be any appropriate cancer treatment. One or more cancer treatments described herein can be administered to a mammal at any appropriate frequency (e.g., once or multiple times over a period of time ranging from days to weeks). Examples of cancer treatments include, without limitation adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors), targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors that target a particular genetic lesion, such as a translocation or mutation), (e.g. a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the above. In some cases, a cancer treatment can reduce the severity of the cancer, reduce a symptom of the cancer, and/or to reduce the number of cancer cells present within the mammal.

In some cases, a cancer treatment can include an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitors include nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (tecentriq), avelumab (bavencio), durvalumab (imfinzi), ipilimumab (yervoy). See, e.g., Pardoll (2012) Nat. Rev Cancer 12: 252-264; Sun et al. (2017) Eur Rev Med Pharmacol Sci 21(6): 1198-1205; Hamanishi et al. (2015) J. Clin. Oncol. 33(34): 4015-22; Brahmer et al. (2012) N Engl J Med 366(26): 2455-65; Ricciuti et al. (2017) J. Thorac Oncol. 12(5): e51-e55; Ellis et al. (2017) Clin Lung Cancer pii: S1525-7304(17)30043-8; Zou and Awad (2017) Ann Oncol 28(4): 685-687; Sorscher (2017) N Engl J Med 376(10: 996-7; Hui et al. (2017) Ann Oncol 28(4): 874-881; Vansteenkiste et al. (2017) Expert Opin Biol Ther 17(6): 781-789; Hellmann et al. (2017) Lancet Oncol. 18(1): 31-41, Chen (2017) J. Chin Med Assoc 80(1): 7-14.

In some cases, a cancer treatment can be an adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors). See, e.g., Rosenberg and Restifo (2015) Science 348(6230): 62-68; Chang and Chen (2017) Trends Mol Med 23(5): 430-450; Yee and Lizee (2016) Cancer J. 23(2): 144-148; Chen et al. (2016) Oncoimmunology 6(2): e1273302; US 2016/0194404; US 2014/0050788; US 2014/0271635; U.S. Pat. No. 9,233,125; incorporated by reference in their entirety herein.

In some cases, a cancer treatment can be a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents include: amsacrine, azacitidine, axathioprine, bevacizumab (or an antigen-binding fragment thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochlorides, etoposide, fiudarabine, floxuridine, fludarabine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mercaptopurine, methotrxate, mitomycin, mitoxantrone, oxaliplatin, paclitaxel, pemetrexed, procarbazine, all-trans retinoic acid, streptozocin, tafluposide, temozolomide, teniposide, tioguanine, topotecan, uramustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine, and combinations thereof. Additional examples of anti-cancer therapies are known in the art; see, e.g. the guidelines for therapy from the American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), or National Comprehensive Cancer Network (NCCN).

When monitoring a mammal having, or suspected of having, cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the monitoring can be before, during, and/or after the course of a cancer treatment. Methods of monitoring provided herein can be used to determine the efficacy of one or more cancer treatments and/or to select a mammal for increased monitoring. In some cases, the monitoring can include identifying a cfDNA fragmentation profile as described herein. For example, a cfDNA fragmentation profile can be obtained before administering one or more cancer treatments to a mammal having, or suspected or having, cancer, one or more cancer treatments can be administered to the mammal, and one or more cfDNA fragmentation profiles can be obtained during the course of the cancer treatment. In some cases, a cfDNA fragmentation profile can change during the course of cancer treatment (e.g., any of the cancer treatments described herein). For example, a cfDNA fragmentation profile indicative that the mammal has cancer can change to a cfDNA fragmentation profile indicative that the mammal does not have cancer. Such a cfDNA fragmentation profile change can indicate that the cancer treatment is working. Conversely, a cfDNA fragmentation profile can remain static (e.g., the same or approximately the same) during the course of cancer treatment (e.g., any of the cancer treatments described herein). Such a static cfDNA fragmentation profile can indicate that the cancer treatment is not working. In some cases, the monitoring can include conventional techniques capable of monitoring one or more cancer treatments (e.g., the efficacy of one or more cancer treatments). In some cases, a mammal selected for increased monitoring can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for increased monitoring can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered two diagnostic tests, whereas a mammal that has not been selected for increased monitoring is administered only a single diagnostic test (or no diagnostic tests). In some cases, a mammal that has been selected for increased monitoring can also be selected for further diagnostic testing. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location (e.g., tissue of origin) of the tumor or the cancer). In some cases, one or more cancer treatments can be administered to the mammal that is selected for increased monitoring after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for increased monitoring can be further monitored, and a cancer treatment can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period. Additionally or alternatively, a mammal that has been selected for increased monitoring can be administered a cancer treatment, and further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for increased monitoring has been administered a cancer treatment, the increased monitoring will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).

When a mammal is identified as having cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the identifying can be before and/or during the course of a cancer treatment. Methods of identifying a mammal as having cancer provided herein can be used as a first diagnosis to identify the mammal (e.g., as having cancer before any course of treatment) and/or to select the mammal for further diagnostic testing. In some cases, once a mammal has been determined to have cancer, the mammal may be administered further tests and/or selected for further diagnostic testing. In some cases, methods provided herein can be used to select a mammal for further diagnostic testing at a time period prior to the time period when conventional techniques are capable of diagnosing the mammal with an early-stage cancer. For example, methods provided herein for selecting a mammal for further diagnostic testing can be used when a mammal has not been diagnosed with cancer by conventional methods and/or when a mammal is not known to harbor a cancer. In some cases, a mammal selected for further diagnostic testing can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for further diagnostic testing can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered two diagnostic tests, whereas a mammal that has not been selected for further diagnostic testing is administered only a single diagnostic test (or no diagnostic tests). In some cases, the diagnostic testing method can determine the presence of the same type of cancer (e.g., having the same tissue or origin) as the cancer that was originally detected (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal). Additionally or alternatively, the diagnostic testing method can determine the presence of a different type of cancer as the cancer that was original detected. In some cases, the diagnostic testing method is a scan. In some cases, the scan is a computed tomography (CT), a CT angiography (CTA), a esophagram (a Barium swallom), a Barium enema, a magnetic resonance imaging (MRI), a PET scan, an ultrasound (e.g., an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan. In some cases, the diagnostic testing method is a physical examination, such as an anoscopy, a bronchoscopy (e.g., an autofluorescence bronchoscopy, a white-light bronchoscopy, a navigational bronchoscopy), a colonoscopy, a digital breast tomosynthesis, an endoscopic retrograde cholangiopancreatography (ERCP), an ensophagogastroduodenoscopy, a mammography, a Pap smear, a pelvic exam, a positron emission tomography and computed tomography (PET-CT) scan. In some cases, a mammal that has been selected for further diagnostic testing can also be selected for increased monitoring. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location of the tumor or the cancer). In some cases, a cancer treatment is administered to the mammal that is selected for further diagnostic testing after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for further diagnostic testing can be administered a further diagnostic test, and a cancer treatment can be administered if the presence of the tumor or the cancer is confirmed. Additionally or alternatively, a mammal that has been selected for further diagnostic testing can be administered a cancer treatment, and can be further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for further diagnostic testing has been administered a cancer treatment, the additional testing will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers (e.g., mutations) will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

Example S Example 1: Cell-Free DIVA Fragmentation in Patients with Cancer

Analyses of cell free DNA have largely focused on targeted sequencing of specific genes. Such studies permit detection of a small number of tumor-specific alterations in patients with cancer and not all patients, especially those with early stage disease, have detectable changes. Whole genome sequencing of cell-free DNA can identify chromosomal abnormalities and rearrangements in cancer patients but detection of such alterations has been challenging in part due to the difficulty in distinguishing a small number of abnormal from normal chromosomal changes (Leary et al., 2010 Sci Transl Med 2:20ra14; and Leary et al., 2012 Sci Transl Med 4:162ra154). Other efforts have suggested nucleosome patterns and chromatin structure may be different between cancer and normal tissues, and that cfDNA in patients with cancer may result in abnormal cfDNA fragment size as well as position (Snyder et al., 2016 Cell 164:57; Jahr et al., 2001 Cancer Res 61:1659; Ivanov et al., 2015 BMC Genomics 16(Suppl 13):S1). However, the amount of sequencing needed for nucleosome footprint analyses of cfDNA is impractical for routine analyses.

The sensitivity of any cell-free DNA approach depends on the number of potential alterations examined as well as the technical and biological limitations of detecting such changes. As a typical blood sample contains ˜2000 genome equivalents of cfDNA per milliliter of plasma (Phallen et al., 2017 Sci Transl Med 9), the theoretical limit of detection of a single alteration can be no better than one in a few thousand mutant to wild-type molecules. An approach that detects a larger number of alterations in the same number of genome equivalents would be more sensitive for detecting cancer in the circulation. Monte Carlo simulations show that increasing the number of potential abnormalities detected from only a few to tens or hundreds can potentially improve the limit of detection by orders of magnitude, similar to recent probability analyses of multiple methylation changes in cfDNA (FIG. 2).

This study presents a novel method called DELFI for detection of cancer and further identification of tissue of origin using whole genome sequencing (FIG. 1). The approach uses cfDNA fragmentation profiles and machine learning to distinguish patterns of healthy blood cell DNA from tumor-derived DNA and to identify the primary tumor tissue. DELFI was used for a retrospective analysis of cfDNA from 245 healthy individuals and 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers, with most patients exhibiting localized disease. Assuming this approach had sensitivity >0.80 for discriminating cancer patients from healthy individuals while maintaining a specificity of 0.95, a study of at least 200 cancer patients would enable estimation of the true sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Materials and Methods Patient and Sample Characteristics

Plasma samples from healthy individuals and plasma and tissue samples from patients with breast, lung, ovarian, colorectal, bile duct, or gastric cancer were obtained from ILSBio/Bioreclamation, Aarhus University, Herlev Hospital of the University of Copenhagen, Hvidovre Hospital, the University Medical Center of the University of Utrecht, the Academic Medical Center of the University of Amsterdam, the Netherlands Cancer Institute, and the University of California, San Diego. All samples were obtained under Institutional Review Board approved protocols with informed consent for research use at participating institutions. Plasma samples from healthy individuals were obtained at the time of routine screening, including for colonoscopies or Pap smears. Individuals were considered healthy if they had no previous history of cancer and negative screening results.

Plasma samples from individuals with breast, colorectal, gastric, lung, ovarian, pancreatic, and bile duct cancer were obtained at the time of diagnosis, prior to tumor resection or therapy. Nineteen lung cancer patients analyzed for change in cfDNA fragmentation profiles across multiple time points were undergoing treatment with anti-EGFR or anti-ERBB2 therapy (see, e.g., Phallen et al., 2019 Cancer Research 15, 1204-1213). Clinical data for all patients included in this study are listed in Table 1 (Appendix A). Gender was confirmed through genomic analyses of X and Y chromosome representation. Pathologic staging of gastric cancer patients was performed after neoadjuvant therapy. Samples where the tumor stage was unknown were indicated as stage X or unknown.

Nucleosomal DNA Purification

Viably frozen lymphocytes were elutriated from leukocytes obtained from a healthy male (C0618) and female (D0808-L) (Advanced Biotechnologies Inc., Eldersburg, Md.). Aliquots of 1×10⁶ cells were used for nucleosomal DNA purification using EZ Nucleosomal DNA Prep Kit (Zymo Research, Irvine, Calif.). Cells were initially treated with 100 μl of Nuclei Prep Buffer and incubated on ice for 5 minutes. After centrifugation at 200 g for 5 minutes, supernatant was discarded and pelleted nuclei were treated twice with 1000 of Atlantis Digestion Buffer or with 100 μl of micrococcal nuclease (MN) Digestion Buffer. Finally, cellular nucleic DNA was fragmented with 0.5 U of Atlantis dsDNase at 42° C. for 20 minutes or 1.5 U of MNase at 37° C. for 20 minutes. Reactions were stopped using 5× MN Stop Buffer and DNA was purified using Zymo-Spin™ IIC Columns. Concentration and quality of eluted cellular nucleic DNA were analyzed using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).

Sample Preparation and Sequencing of cfDNA

Whole blood was collected in EDTA tubes and processed immediately or within one day after storage at 4° C., or was collected in Streck tubes and processed within two days of collection for three cancer patients who were part of the monitoring analysis. Plasma and cellular components were separated by centrifugation at 800 g for 10 min at 4° C. Plasma was centrifuged a second time at 18,000 g at room temperature to remove any remaining cellular debris and stored at −80° C. until the time of DNA extraction. DNA was isolated from plasma using the Qiagen Circulating Nucleic Acids Kit (Qiagen GmbH) and eluted in LoBind tubes (Eppendorf AG). Concentration and quality of cfDNA were assessed using the Bioanalyzer 2100 (Agilent Technologies).

NGS cfDNA libraries were prepared for whole genome sequencing and targeted sequencing using 5 to 250 ng of cfDNA as described elsewhere (see, e.g., Phallen et al, 2017 Sci Transl Med 9:eaan2415). Briefly, genomic libraries were prepared using the NEBNext DNA Library Prep Kit for Illumina [New England Biolabs (NEB)] with four main modifications to the manufacturer's guidelines: (i) The library purification steps used the on-bead AMPure XP approach to minimize sample loss during elution and tube transfer steps (see, e.g., Fisher et al., 2011 Genome Biol 12:R1); (ii) NEBNext End Repair, A-tailing, and adapter ligation enzyme and buffer volumes were adjusted as appropriate to accommodate the on-bead AMPure XP purification strategy; (iii) a pool of eight unique Illumina dual index adapters with 8-base pair (bp) barcodes was used in the ligation reaction instead of the standard Illumina single or dual index adapters with 6- or 8-bp barcodes, respectively; and (iv) cfDNA libraries were amplified with Phusion Hot Start Polymerase.

Whole genome libraries were sequenced directly. For targeted libraries, capture was performed using Agilent SureSelect reagents and a custom set of hybridization probes targeting 58 genes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415) per the manufacturer's guidelines. The captured library was amplified with Phusion Hot Start Polymerase (NEB). Concentration and quality of captured cfDNA libraries were assessed on the Bioanalyzer 2100 using the DNA1000 Kit (Agilent Technologies). Targeted libraries were sequenced using 100-bp paired-end runs on the Illumina HiSeq 2000/2500 (Illumina).

Analyses of Targeted Sequencing Data from cfDNA

Analyses of targeted NGS data for cfDNA samples was performed as described elsewhere (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415). Briefly, primary processing was completed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg18 or hg19) using NovoAlign with additional realignment of select regions using the Needleman-Wunsch method (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53). The positions of the sequence alterations have not been affected by the different genome builds. Candidate mutations, consisting of point mutations, small insertions, and deletions, were identified using VariantDx (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53) (Personal Genome Diagnostics, Baltimore, Md.) across the targeted regions of interest.

To analyze the fragment lengths of cfDNA molecules, each read pair from a cfDNA molecule was required to have a Phred quality score ≥30. All duplicate cfDNA fragments, defined as having the same start, end, and index barcode were removed. For each mutation, only fragments for which one or both of the read pairs contained the mutated (or wild-type) base at the given position were included. This analysis was done using the R packages Rsamtools and GenomicAlignments.

For each genomic locus where a somatic mutation was identified, the lengths of fragments containing the mutant allele were compared to the lengths of fragments of the wild-type allele. If more than 100 mutant fragments were identified, Welch's two-sample t-test was used to compare the mean fragment lengths. For loci with fewer than 100 mutant fragments, a bootstrap procedure was implemented. Specifically, replacement N fragments containing the wild-type allele, where N denotes the number of fragments with the mutation, were sampled. For each bootstrap replicate of wild type fragments their median length was computed. The p-value was estimated as the fraction of bootstrap replicates with a median wild-type fragment length as or more extreme than the observed median mutant fragment length.

Analyses of Whole Genome Sequencing Data from cfDNA

Primary processing of whole genome NGS data for cfDNA samples was performed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8.2), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg19) using ELAND.

Read pairs with a MAPQ score below 30 for either read and PCR duplicates were removed. hg19 autosomes were filed into 26,236 adjacent, non-overlapping 100 kb bins. Regions of low mappability, indicated by the 10% of bins with the lowest coverage, were removed (see, e.g., Fortin et al., 2015 Genome Biol 16:180), as were reads falling in the Duke blacklisted regions (see, e.g., hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeMapability/). Using this approach, 361 Mb (13%) of the hg19 reference genome was excluded, including centromeric and telomeric regions. Short fragments were defined as having a length between 100 and 150 bp and long fragments were defined has having a length between 151 and 220 bp.

To account for biases in coverage attributable to GC content of the genome, the locally weighted smoother loess with span 3/4 was applied to the scatterplot of average fragment GC versus coverage calculated for each 100 kb bin. This loess regression was performed separately for short and long fragments to account for possible differences in GC effects on coverage in plasma by fragment length (see, e.g., Benjamini et al., 2012 Nucleic Acids Res 40:e72). The predictions for short and long coverage explained by GC from the loess model were subtracted, obtaining residuals for short and long that were uncorrelated with GC. The residuals were returned to the original scale by adding back the genome-wide median short and long estimates of coverage. This procedure was repeated for each sample to account for possible differences in GC effects on coverage between samples. To further reduce the feature space and noise, the total GC-adjusted coverage in 5 Mb bins was calculated.

To compare the variability of fragment lengths from healthy subjects to fragments in patients with cancer, the standard deviation of the short to long fragmentation profiles for each individual was calculated. The standard deviations in the two groups were compared by a Wilcoxon rank sum test.

Analyses of Chromosome Arm Copy Number Changes

To develop arm-level statistics for copy number changes, an approach for aneuploidy detection in plasma as described elsewhere (see, e.g., Leary et al., 2012 Sci Transl Med 4:162ra154) was adopted. This approach divides the genome into non-overlapping 50 KB bins for which GC-corrected log 2 read depth was obtained after correction by loess with span 3/4. This loess-based correction is comparable to the approach outlined above, but is evaluated on a log 2 scale to increase robustness to outliers in the smaller bins and does not stratify by fragment length. To obtain an arm-specific Z-score for copy number changes, the mean GC-adjusted read depth for each arm (GR) was centered and scaled by the average and standard deviation, respectively, of GR scores obtained from an independent set of 50 healthy samples.

Analyses of Mitochondrial-Aligned Reads from cfDNA

Whole genome sequence reads that initially mapped to the mitochondrial genome were extracted from barn files and realigned to the hg19 reference genome in end-to-end mode with Bowtie2 as described elsewhere (see, e.g., Langmead et al., 2012 Nat Methods 9:357-359). The resulting aligned reads were filtered such that both mates aligned to the mitochondrial genome with MAPQ >=30. The number of fragments mapping to the mitochondrial genome was counted and converted to a percentage of the total number of fragments in the original barn files.

Prediction Model for Cancer Classification

To distinguish healthy from cancer patients using fragmentation profiles, a stochastic gradient boosting model was used (gbm; see, e.g., Friedman et al., 2001 Ann Stat 29:1189-1232; and Friedman et al., 2002 Comput Stat Data An 38:367-378). GC-corrected total and short fragment coverage for all 504 bins were centered and scaled for each sample to have mean 0 and unit standard deviation. Additional features included Z-scores for each of the 39 autosomal arms and mitochondrial representation (log 10-transformed proportion of reads mapped to the mitochondria). To estimate the prediction error of this approach, 10-fold cross-validation was used as described elsewhere (see, e.g., Efron et al., 1997 J Am Stat Assoc 92, 548-560). Feature selection, performed only on the training data in each cross-validation run, removed bins that were highly correlated (correlation >0.9) or had near zero variance. Stochastic gradient boosted machine learning was implemented using the R package gbm package with parameters n.trees=150, interaction.depth=3, shrinkage=0.1, and n.minobsinside=10. To average over the prediction error from the randomization of patients to folds, the 10-fold cross validation procedure was repeated 10 times. Confidence intervals for sensitivity fixed at 98% and 95% specificity were obtained from 2000 bootstrap replicates.

Prediction Model for Tumor Tissue of Origin Classification

For samples correctly classified as cancer patients at 90% specificity (n=174), a separate stochastic gradient boosting model was trained to classify the tissue of origin. To account for the small number of lung samples used for prediction, 18 cfDNA baseline samples from late stage lung cancer patients were included from the monitoring analyses. Performance characteristics of the model were evaluated by 10-fold cross-validation repeated 10 times. This gbm model was trained using the same features as in the cancer classification model. As previously described, features that displayed correlation above 0.9 to each other or had near zero variance were removed within each training dataset during cross-validation. The tissue class probabilities were averaged across the 10 replicates for each patient and the class with the highest probability was taken as the predicted tissue.

Analyses of Nucleosomal DNA from Human Lymphocytes and cfDNA

From the nuclease treated lymphocytes, fragment sizes were analyzed in 5 Mb bins as described for whole genome cfDNA analyses. A genome-wide map of nucleosome positions was constructed from the nuclease treated lymphocyte cell-lines. This approach identified local biases in the coverage of circulating fragments, indicating a region protected from degradation. A “Window positioning score” (WPS) was used to score each base pair in the genome (see, e.g., Snyder et al., 2016 Cell 164:57). Using a sliding window of 60 bp centered around each base, the WPS was calculated as the number of fragments completely spanning the window minus the number of fragments with only one end in the window. Since fragments arising from nucleosomes have a median length of 167 bp, a high WPS indicated a possible nucleosomic position. WPS scores were centered at zero using a running median and smoothed using a Kolmogorov-Zurbenko filter (see, e.g., Zurbenko, The spectral analysis of time series. North-Holland series in statistics and probability; Elsevier, New York, N Y, 1986). For spans of positive WPS between 50 and 450 bp, a nucleosome peak was defined as the set of base pairs with a WPS above the median in that window. The calculation of nucleosome positions for cfDNA from 30 healthy individuals with sequence coverage of 9× was determined in the same manner as for lymphocyte DNA. To ensure that nucleosomes in healthy cfDNA were representative, a consensus track of nucleosomes was defined consisting only of nucleosomes identified in two or more individuals. Median distances between adjacent nucleosomes were calculated from the consensus track.

Monte Carlo Simulation of Detection Sensitivity

A Monte Carlo simulation was used to estimate the probability of detecting a molecule with a tumor-derived alteration. Briefly, 1 million molecules were generated from a multinomial distribution. For a simulation with m alterations, wild-type molecules were simulated with probability p and each of the m tumor alterations were simulated with probability (1−p)/m. Next, g*m molecules were sampled randomly with replacement, where g denotes the number of genome equivalents in 1 ml of plasma. If a tumor alteration was sampled s or more times, the sample was classified as cancer-derived. The simulation was repeated 1000 times, estimating the probability that the in silico sample would be correctly classified as cancer by the mean of the cancer indicator. Setting g=2000 and s=5, the number of tumor alterations was varied by powers of 2 from 1 to 256 and the fraction of tumor-derived molecules from 0.0001% to 1%.

Statistical Analyses

All statistical analyses were performed using R version 3.4.3. The R packages caret (version 6.0-79) and gbm (version 2.1-4) were used to implement the classification of healthy versus cancer and tissue of origin. Confidence intervals from the model output were obtained with the pROC (version 1.13) R package (see, e.g., Robin et al., 2011 BMC bioinformatics 12:77). Assuming the prevalence of undiagnosed cancer cases in this population is high (1 or 2 cases per 100 healthy), a genomic assay with a specificity of 0.95 and sensitivity of 0.8 would have useful operating characteristics (positive predictive value of 0.25 and negative predictive value near 1). Power calculations suggest that an analysis of more than 200 cancer patients and an approximately equal number of healthy controls, enable an estimation of the sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Data and Code Availability

Sequence data utilized in this study have been deposited at the European Genome-phenome Archive under study accession nos. EGAS00001003611 and EGAS00001002577. Code for analyses is available at github.com/Cancer-Genomics/delfi_scripts.

Results

DELFI allows simultaneous analysis of a large number of abnormalities in cfDNA through genome-wide analysis of fragmentation patterns. The method is based on low coverage whole genome sequencing and analysis of isolated cfDNA. Mapped sequences are analyzed in non-overlapping windows covering the genome. Conceptually, windows may range in size from thousands to millions of bases, resulting in hundreds to thousands of windows in the genome. 5 Mb windows were used for evaluating cfDNA fragmentation patterns as these would provide over 20,000 reads per window even at a limited amount of 1-2× genome coverage. Within each window, the coverage and size distribution of cfDNA fragments was examined. This approach was used to evaluate the variation of genome-wide fragmentation profiles in healthy and cancer populations (Table 1; Appendix A). The genome-wide pattern from an individual can be compared to reference populations to determine if the pattern is likely healthy or cancer-derived. As genome-wide profiles reveal positional differences associated with specific tissues that may be missed in overall fragment size distributions, these patterns may also indicate the tissue source of cfDNA.

The fragmentation size of cfDNA was focused on as it was found that cancer-derived cfDNA molecules may be more variable in size than cfDNA derived from non-cancer cells. cfDNA fragments from targeted regions that were captured and sequenced at high coverage (43,706 total coverage, 8,044 distinct coverage) from patients with breast, colorectal, lung or ovarian cancer (Table 1 (Appendix A), Table 2 (Appendix B), and Table 3 (Appendix C)) were initially examined. Analyses of loci containing 165 tumor-specific alterations from 81 patients (range of 1-7 alterations per patient) revealed an average absolute difference of 6.5 bp (95% CI, 5.4-7.6 bp) between lengths of median mutant and wild-type cfDNA fragments (FIG. 3, Table 3 (Appendix C)). The median size of mutant cfDNA fragments ranged from 30 bases smaller at chromosome 3 position 41,266,124 to 47 bases larger at chromosome 11 position 108,117,753 than the wild-type sequences at these regions (Table 3; Appendix C). GC content was similar for mutated and non-mutated fragments (FIG. 4a ), and there was no correlation between GC content and fragment length (FIG. 4b ). Similar analyses of 44 germline alterations from 38 patients identified median cfDNA size differences of less than 1 bp between fragment lengths of different alleles (FIG. 5, Table 3 (Appendix C)). Additionally, 41 alterations related to clonal hematopoiesis were identified through a previous sequence comparison of DNA from plasma, buffy coat, and tumors of the same individuals. Unlike tumor-derived fragments, there were no significant differences between fragments with hematopoietic alterations and wild type fragments (FIG. 6, Table 3 (Appendix C)). Overall, cancer-derived cfDNA fragment lengths were significantly more variable compared to non-cancer cfDNA fragments at certain genomic regions (p<0.001, variance ratio test). It was hypothesized that these differences may be due to changes in higher-order chromatin structure as well as other genomic and epigenomic abnormalities in cancer and that cfDNA fragmentation in a position-specific manner could therefore serve as a unique biomarker for cancer detection.

As targeted sequencing only analyzes a limited number of loci, larger-scale genome-wide analyses to detect additional abnormalities in cfDNA fragmentation were investigated. cfDNA was isolated from ˜4 ml of plasma from 8 lung cancer patients with stage I-III disease, as well as from 30 healthy individuals (Table 1 (Appendix A), Table 4 (Appendix D), and Table 5 (Appendix E)). A high efficiency approach was used to convert cfDNA to next generation sequencing libraries and performed whole genome sequencing at ˜9× coverage (Table 4; Appendix D). Overall cfDNA fragment lengths of healthy individuals were larger, with a median fragment size of 167.3 bp, while patients with cancer had median fragment sizes of 163.8 (p<0.01, Welch's t-test) (Table 5; Appendix E). To examine differences in fragment size and coverage in a position dependent manner across the genome, sequenced fragments were mapped to their genomic origin and fragment lengths were evaluated in 504 windows that were 5 Mb in size, covering ˜2.6 Gb of the genome. For each window, the fraction of small cfDNA fragments (100 to 150 bp in length) to larger cfDNA fragments (151 to 220 bp) as well as overall coverage were determined and used to obtain genome-wide fragmentation profiles for each sample.

Healthy individuals had very similar fragmentation profiles throughout the genome (FIG. 7 and FIG. 8). To examine the origins of fragmentation patterns normally observed in cfDNA, nuclei were isolated from elutriated lymphocytes of two healthy individuals and treated with DNA nucleases to obtain nucleosomal DNA fragments. Analyses of cfDNA patterns in observed healthy individuals revealed a high correlation to lymphocyte nucleosomal DNA fragmentation profiles (FIGS. 7b and 7d ) and nucleosome distances (FIGS. 7c and 7f ). Median distances between nucleosomes in lymphocytes were correlated to open (A) and closed (B) compartments of lymphoblastoid cells as revealed using the Hi-C method (see, e.g., Lieberman-Aiden et al., 2009 Science 326:289-293; and Fortin et al., 2015 Genome Biol 16:180) for examining the three-dimensional architecture of genomes (FIG. 7c ). These analyses suggest that the fragmentation patterns of normal cfDNA are the result of nucleosomal DNA patterns that largely reflect the chromatin structure of normal blood cells.

In contrast to healthy cfDNA, patients with cancer had multiple distinct genomic differences with increases and decreases in fragment sizes at different regions (FIGS. 7a and 7b ). Similar to our observations from targeted analyses, there was also greater variation in fragment lengths genome-wide for patients with cancer compared to healthy individuals.

To determine whether cfDNA fragment length patterns could be used to distinguish patients with cancer from healthy individuals, genome-wide correlation analyses were performed of the fraction of short to long cfDNA fragments for each sample compared to the median fragment length profile calculated from healthy individuals (FIGS. 7a, 7b, and 7e ). While the profiles of cfDNA fragments were remarkably consistent among healthy individuals (median correlation of 0.99), the median correlation of genome-wide fragment ratios among cancer patients was 0.84 (0.15 lower, 95% CI 0.07-0.50, p<0.001, Wilcoxon rank sum test; Table 5 (Appendix E)). Similar differences were observed when comparing fragmentation profiles of cancer patients to fragmentation profiles or nucleosome distances in healthy lymphocytes (FIGS. 7c, 7d, and 7f ). To account for potential biases in the fragmentation profiles attributable to GC content, a locally weighted smoother was applied independently to each sample and found that differences in fragmentation profiles between healthy individuals and cancer patients remained after this adjustment (median correlation of cancer patientsto healthy=0.83) (Table 5; Appendix E).

Subsampling analyses of whole genome sequence data was performed at 9× coverage from cfDNA of patients with cancer at ˜2×, ˜1×, ˜0.5×, ˜0.2×, and ˜0.1× genome coverage, and it was determined that altered fragmentation profiles were readily identified even at 0.5× genome coverage (FIG. 9). Based on these observations, whole genome sequencing was performed with coverage of 1-2× to evaluate whether fragmentation profiles may change during the course of targeted therapy in a manner similar to monitoring of sequence alterations. cfDNA from 19 non-small cell lung cancer patients including 5 with partial radiographic response, 8 with stable disease, 4 with progressive disease, and 2 with unmeasurable disease, during the course of anti-EGFR or anti-ERBB2 therapy was evaluated (Table 6; Appendix F). As shown in FIG. 10, the degree of abnormality in the fragmentation profiles during therapy closely matched levels of EGFR or ERBB2 mutant allele fractions as determined using targeted sequencing (Spearman correlation of mutant allele fractions to fragmentation profiles=0.74). This correlation is remarkable as genome-wide and mutation-based methods are orthogonal and examine different cfDNA alterations that may be suppressed in these patients due to prior therapy. Notably all cases that had progression free survival of six or more months displayed a drop of or had extremely low levels of cfDNA after initiation of therapy as determined by fragmentation profiles, while cases with poor clinical outcome had increases in cfDNA. These results demonstrate the feasibility of fragmentation analyses for detecting the presence of tumor-derived cfDNA, and suggests that such analyses may also be useful for quantitative monitoring of cancer patients during treatment.

The fragmentation profiles were examined in the context of known copy number changes in a patient where parallel analyses of tumor tissue were obtained. These analyses demonstrated that altered fragmentation profiles were present in regions of the genome that were copy neutral and that these may be further affected in regions with copy number changes (FIG. 11a and FIG. 12a ). Position dependent differences in fragmentation patterns could be used to distinguish cancer-derived cfDNA from healthy cfDNA in these regions (FIG. 12a, b ), while overall cfDNA fragment size measurements would have missed such differences (FIG. 12a ).

These analyses were extended to an independent cohort of cancer patients and healthy individuals. Whole genome sequencing of cfDNA at 1-2× coverage from a total of 208 patients with cancer, including breast (n=54), colorectal (n=27), lung (n=12), ovarian (n=28), pancreatic (n=34), gastric (n=27), or bile duct cancers (n=26), as well as 215 individuals without cancer was performed (Table 1 (Appendix A) and Table 4 (Appendix D)). All cancer patients were treatment naïve and the majority had resectable disease (n=183). After GC adjustment of short and long cfDNA fragment coverage (FIG. 13a ), coverage and size characteristics of fragments in windows throughout the genome were examined (FIG. 11b , Table 4 (Appendix D) and Table 7 (Appendix G)). Genome-wide correlations of coverage to GC content were limited and no differences in these correlations between cancer patients and healthy individuals were observed (FIG. 13b ). Healthy individuals had highly concordant fragmentation profiles, while patients with cancer had high variability with decreased correlation to the median healthy profile (Table 7; Appendix G). An analysis of the most commonly altered fragmentation windows in the genome among cancer patients revealed a median of 60 affected windows across the cancer types analyzed, highlighting the multitude of position dependent alterations in fragmentation of cfDNA in individuals with cancer (FIG. 11c ).

To determine if position dependent fragmentation changes can be used to detect individuals with cancer, a gradient tree boosting machine learning model was implemented to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual and estimated performance characteristics of this approach by ten-fold cross validation repeated ten times (FIGS. 14 and 15). The machine learning model included GC-adjusted short and long fragment coverage characteristics in windows throughout the genome. A machine learning classifier for copy number changes from chromosomal arm dependent features rather than a single score was also developed (FIG. 16a and Table 8 (Appendix H)) and mitochondrial copy number changes were also included (FIG. 16b ) as these could also help distinguish cancer from healthy individuals. Using this implementation of DELFI, a score was obtained that could be used to classify patients as healthy or having cancer. 152 of the 208 cancer patients were detected (73% sensitivity, 95% CI 67%-79%) while four of the 215 healthy individuals were misclassified (98% specificity) (Table 9). At a threshold of 95% specificity, 80% of patients with cancer were detected (95% CI, 74%-85%), including 79% of resectable (stage I-III) patients (145 of 183) and 82% of metastatic (stage IV) patients (18 out of 22) (Table 9). Receiver operator characteristic analyses for detection of patients with cancer had an AUC of 0.94 (95% CI 0.92-0.96), ranged among cancer types from 0.86 for pancreatic cancer to >0.99 for lung and ovarian cancers (FIGS. 17a and 17b ), and had AUCs>0.92 across all stages (FIG. 18). The DELFI classifier score did not differ with age among either cancer patients or healthy individuals (Table 1; Appendix A).

TABLE 9 DELFI performance for cancer detection. 95% specificity 98% specificity Individuals Individuals Individuals analyzed detected Sensitivity 95% CI detected Sensitivity 95% CI Healthy 215 10 — — 4 — — Cancer 208 166 80% 74%-85% 152 73% 67%-79% Type Breast 54 38 70% 56%-82% 31 57% 43%-71% Bile duct 26 23 88% 70%-98% 21 81% 61%-93% Colorectal 27 22 81% 62%-94% 19 70% 50%-86% Gastric 27 22 81% 62%-94% 22 81% 62%-94% Lung 12 12 100%   74%-100% 12 100%   74%-100% Ovarian 28 25 89% 72%-98% 25 89% 72%-98% Pancreatic 34 24 71% 53%-85% 22 65% 46%-80% Stage I 41 30 73% 53%-86% 28 68% 52%-82% II 109 85 78% 69%-85% 78 72% 62%-80% III 33 30 91% 76%-98% 26 79% 61%-91% IV 22 18 82% 60%-95% 17 77% 55%-92% 0, X 3 3 100%   29%-100% 3 100%   29%-100%

To assess the contribution of fragment size and coverage, chromosome arm copy number, or mitochondrial mapping to the predictive accuracy of the model, the repeated 10-fold cross-validation procedure was implemented to assess performance characteristics of these features in isolation. It was observed that fragment coverage features alone (AUC=0.94) were nearly identical to the classifier that combined all features (AUC=0.94) (FIG. 17a ). In contrast, analyses of chromosomal copy number changes had lower performance (AUC=0.88) but were still more predictive than copy number changes based on individual scores (AUC=0.78) or mitochondrial mapping (AUC=0.72) (FIG. 17a ). These results suggest that fragment coverage is the major contributor to our classifier. Including all features in the prediction model may contribute in a complementary fashion for detection of patients with cancer as they can be obtained from the same genome sequence data.

As fragmentation profiles reveal regional differences in fragmentation that may differ between tissues, a similar machine learning approach was used to examine whether cfDNA patterns could identify the tissue of origin of these tumors. It was found that this approach had a 61% accuracy (95% CI 53%-67%), including 76% for breast, 44% for bile duct, 71% for colorectal, 67% for gastric, 53% for lung, 48% for ovarian, and 50% for pancreatic cancers (FIG. 19, Table 10). The accuracy increased to 75% (95% CI 69%-81%) when considering assigning patients with abnormal cfDNA to one of two sites of origin (Table 10). For all tumor types, the classification of the tissue of origin by DELFI was significantly higher than determined by random assignment (p<0.01, binomial test, Table 10).

TABLE 10 DELFI tissue of origin prediction Cancer Patients Top Prediction Top Two Predictions Random Assignment Type Detected* Patients Accuracy (95% CI) Patients Accuracy (95% CI) Patients Accuracy Breast 42 32 76% (61%-88%) 38 91% (77%-97%) 9 22% Bile Duct 23 10 44% (23%-66%) 15 65% (43%-84%) 3 12% Colorectal 24 17 71% (49%-87%) 19 79% (58%-93%) 3 12% Gastric 24 16 67% (45%-84%) 19 79% (58%-93%) 3 12% Lung 30 16 53% (34%-72%) 23 77% (58%-90%) 2  6% Ovarian 27 13 48% (29%-68%) 16 59% (38%-78%) 4 14% Pancreatic 24 12 50% (29%-71%) 16 67% (45%-84%) 3 12% Total 194 116 61% (53%-67%) 146 75% (69%-81%) 26 13% *Patients detected are based on DELFI detection at 90% specificity. Lung cohort includes additional lung cancer patients with prior therapy.

As cancer-specific sequence alterations can be used to identify patients with cancer, it was evaluated whether combining DELFI with this approach could increase the sensitivity of cancer detection (FIG. 20). An analysis of cfDNA from a subset of the treatment naïve cancer patients using both DELFI and targeted sequencing revealed that 82% (103 of 126) of patients had fragmentation profile alterations, while 66% (83 of 126) had sequence alterations. Over 89% of cases with mutant allele fractions >1% were detected by DELFI while for cases with mutant allele fractions <1% the fraction detected by DELFI was 80%, including for cases that were undetectable using targeted sequencing (Table 7; Appendix G). When these approaches were used together, the combined sensitivity of detection increased to 91% (115 of 126 patients) with a specificity of 98% (FIG. 20).

Overall, genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. The variability in fragment lengths and coverage in a position dependent manner throughout the genome may explain the apparently contradictory observations of previous analyses of cfDNA at specific loci or of overall fragment sizes. In patients with cancer, heterogeneous fragmentation patterns in cfDNA appear to be a result of mixtures of nucleosomal DNA from both blood and neoplastic cells. These studies provide a method for simultaneous analysis of tens to potentially hundreds of tumor-specific abnormalities from minute amounts of cfDNA, overcoming a limitation that has precluded the possibility of more sensitive analyses of cfDNA. DELFI analyses detected a higher fraction of cancer patients than previous cfDNA analysis methods that have focused on sequence or overall fragmentation sizes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; Bettegowda et al., 2014 Sci Transl Med 6:224ra24; Newman et al., 2016 Nat Biotechnol 34:547). As demonstrated in this Example, combining DELFI with analyses of other cfDNA alterations may further increase the sensitivity of detection. As fragmentation profiles appear related to nucleosomal DNA patterns, DELFI may be used for determining the primary source of tumor-derived cfDNA. The identification of the source of circulating tumor DNA in over half of patients analyzed may be further improved by including clinical characteristics, other biomarkers, including methylation changes, and additional diagnostic approaches (Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung & circulation 20:634; Cohen et al., 2018 Science 359:926). Finally, this approach requires only a small amount of whole genome sequencing, without the need for deep sequencing typical of approaches that focus on specific alterations. The performance characteristics and limited amount of sequencing needed for DELFI suggests that our approach could be broadly applied for screening and management of patients with cancer.

These results demonstrate that genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. As such, cfDNA fragmentation profiles can have important implications for future research and applications of non-invasive approaches for detection of human cancer.

Other Embodiments

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

TABLE 1 APPENDIX A: Summary of patients and samples analyzed Age at Sample Diag- Gen- TNM Site of Patient Patient Type Type Timepoint nosis der Stage Staging Primary Tumor CGCRC321 Colo

ectal Cancer cfDNA Preoperative treatment naive 6

F IV T3N2M1 Coecum CGCRC232 Colo

ectal Cancer cfDNA Preoperative treatment naive 51 M IV T3N2M1 S

gmo

d Colon CGCRC29

Colo

ectal Cancer cfDNA Preoperative treatment naive 55 M IV T3N2M1 Rectum CGCRC294 Colo

ectal Cancer cfDNA Preoperative treatment naive 67 F II T3N0M0 S

gmo

d Colon CGCRC296 Colo

ectal Cancer cfDNA Preoperative treatment naive 76 F II T4N0M0 Coecum CGCRC299 Colo

ectal Cancer cfDNA Preoperative treatment naive 71 M I T1N0M0 Rectum CGCRC3

0 Colo

ectal Cancer cfDNA Preoperative treatment naive 65 M I T2N0M0 Rectum CGCRC3

1 Colo

ectal Cancer cfDNA Preoperative treatment naive 76 F I T2N0M0 Rectum CGCRC3

2 Colo

ectal Cancer cfDNA Preoperative treatment naive 73 M II T3N0M0 Transverse Colon CGCRC3

4 Colo

ectal Cancer cfDNA Preoperative treatment naive 86 F II T3N0M0 Rectum CGCRC3

5 Colo

ectal Cancer cfDNA Preoperative treatment naive 83 F II T3N0M0 Transverse Colon CGCRC3

6 Colo

ectal Cancer cfDNA Preoperative treatment naive 80 F II T4N0M0 Ascending Colon CGCRC3

7 Colo

ectal Cancer cfDNA Preoperative treatment naive 78 F II T3N0M0 Ascending Colon CGCRC3

Colo

ectal Cancer cfDNA Preoperative treatment naive 72 F III T4N2M0 Ascending Colon CGCRC311 Colo

ectal Cancer cfDNA Preoperative treatment naive 59 M I T2N0M0 S

gmo

d Colon CGCRC315 Colo

ectal Cancer cfDNA Preoperative treatment naive 74 M III T3N1M0 S

gmo

d Colon CGCRC316 Colo

ectal Cancer cfDNA Preoperative treatment naive 80 M III T3N2M0 Transverse Colon CGCRC317 Colo

ectal Cancer cfDNA Preoperative treatment naive 74 M III T3N2M0 Descending Colon CGCRC318 Colo

ectal Cancer cfDNA Preoperative treatment naive 81 M I T2N0M0 Coecum CGCRC319 Colo

ectal Cancer cfDNA Preoperative treatment naive 80 F III T2N1M0 Descending Colon CGCRC320 Colo

ectal Cancer cfDNA Preoperative treatment naive 73 F I T2N0M0 Ascending Colon CGCRC321 Colo

ectal Cancer cfDNA Preoperative treatment naive 68 M I T2N0M0 Rectum CGCRC333 Colo

ectal Cancer cfDNA Preoperative treatment naive NA F IV NA Colon/Rectum CGCRC336 Colo

ectal Cancer cfDNA Preoperative treatment naive NA M IV NA Colon/Rectum CGCRC338 Colo

ectal Cancer cfDNA Preoperative treatment naive NA F IV NA Colon/Rectum CGCRC341 Colo

ectal Cancer cfDNA Preoperative treatment naive NA F IV NA Colon/Rectum CGCRC342 Colo

ectal Cancer cfDNA Preoperative treatment naive NA M IV NA Colon/Rectum CGLU316 Lung Cancer cfDNA P

e-treatment, Day −53 50 F IV T3N2M0 Left Upper Lobe of Lung CGLU316 Lung Cancer cfDNA Pre-treatment, Day −4 50 F IV T3N2M0 Left Upper Lobe of Lung CGLU316 Lung Cancer cfDNA Post-treatment, Day 18 50 F IV T3N2M0 Left Upper Lobe of Lung CGLU316 Lung Cancer cfDNA Post-treatment, Day 87 50 F IV T3N2M0 Left Upper Lobe of Lung CGLU344 Lung Cancer cfDNA Pre-treatment, Day −21 65 F IV T2N2M1 Right Upper Lobe of Lung CGLU344 Lung Cancer cfDNA Pre-treatment, Day 0 65 F IV T2N2M1 Right Upper Lobe of Lung CGLU344 Lung Cancer cfDNA Post-treatment, Day 0.

875 65 F IV T2N2M1 Right Upper Lobe of Lung CGLU344 Lung Cancer cfDNA Post-treatment, Day

65 F IV T2N2M1 Right Upper Lobe of Lung CGLU369 Lung Cancer cfDNA Pre-treatment, Day −2 48 F IV T2NxM1 Right Upper Lobe of Lung CGLU369 Lung Cancer cfDNA Post-treatment, Day 12 48 F IV T2NxM1 Right Upper Lobe of Lung CGLU369 Lung Cancer cfDNA Post-treatment, Day

8 48 F IV T2NxM1 Right Upper Lobe of Lung CGLU369 Lung Cancer cfDNA Post-treatment, Day 110 48 F IV T2NxM1 Right Upper Lobe of Lung CGLU373 Lung Cancer cfDNA Pre-treatment, Day −2 56 F IV T3N1M0 Right Upper Lobe of Lung CGLU373 Lung Cancer cfDNA Post-treatment, Day 0.12

56 F IV T3N1M0 Right Upper Lobe of Lung CGLU373 Lung Cancer cfDNA Post-treatment, Day 7 56 F IV T3N1M0 Right Upper Lobe of Lung CGLU373 Lung Cancer cfDNA Post-treatment, Day 47 56 F IV T3N1M0 Right Upper Lobe of Lung CGPLBR100 Breast Cancer cfDNA Preoperative treatment naive 44 F III T2N2M0 Left Breast CGPLBR101 Breast Cancer cfDNA Preoperative treatment naive 46 F II T2N1M0 Left Breast CGPLBR102 Breast Cancer cfDNA Preoperative treatment naive 47 F II T2N1M0 Right Breast CGPLBR103 Breast Cancer cfDNA Preoperative treatment naive 48 F II T2N1M0 Left Breast CGPLBR104 Breast Cancer cfDNA Preoperative treatment naive 68 F II T2N0M0 Right Breast CGPLBR12 Breast Cancer cfDNA Preoperative treatment naive NA F III NA Breast CGPLBR18 Breast Cancer cfDNA Preoperative treatment naive NA F III NA Breast CGPLBR23 Breast Cancer cfDNA Preoperative treatment naive 53 F II NA Breast CGPLBR24 Breast Cancer cfDNA Preoperative treatment naive 52 F II NA Breast CGPLBR28 Breast Cancer cfDNA Preoperative treatment naive 59 F III NA Breast CGPLBR30 Breast Cancer cfDNA Preoperative treatment naive 61 F II NA Breast CGPLBR31 Breast Cancer cfDNA Preoperative treatment naive 54 F II NA Breast CGPLBR32 Breast Cancer cfDNA Preoperative treatment naive NA F II NA Breast CGPLBR33 Breast Cancer cfDNA Preoperative treatment naive 47 F II NA Breast CGPLBR34 Breast Cancer cfDNA Preoperative treatment naive 60 F II NA Breast CGPLBR35 Breast Cancer cfDNA Preoperative treatment naive 43 F II NA Breast CGPLBR36 Breast Cancer cfDNA Preoperative treatment naive 36 F II NA Breast CGPLBR37 Breast Cancer cfDNA Preoperative treatment naive 58 F II NA Breast CGPLBR38 Breast Cancer cfDNA Preoperative treatment naive 54 F I T1N0M0 Left Breast CGPLBR40 Breast Cancer cfDNA Preoperative treatment naive 66 F III T2N2M0 Left Breast CGPLBR41 Breast Cancer cfDNA Preoperative treatment naive 51 F III T3N1M0 Left Breast CGPLBR45 Breast Cancer cfDNA Preoperative treatment naive 57 F II NA Breast CGPLBR46 Breast Cancer cfDNA Preoperative treatment naive 54 F III NA Breast CGPLBR47 Breast Cancer cfDNA Preoperative treatment naive 54 F I NA Breast CGPLBR48 Breast Cancer cfDNA Preoperative treatment naive 47 F II T2N1M0 Left Breast CGPLBR49 Breast Cancer cfDNA Preoperative treatment naive 37 F II T2N1M0 Left Breast CGPLBR50 Breast Cancer cfDNA Preoperative treatment naive 51 F I NA Breast CGPLBR51 Breast Cancer cfDNA Preoperative treatment naive 53 F II NA Breast CGPLBR52 Breast Cancer cfDNA Preoperative treatment naive 68 F III NA Breast CGPLBR55 Breast Cancer cfDNA Preoperative treatment naive 53 F III T3N1M0 Right Breast CGPLBR56 Breast Cancer cfDNA Preoperative treatment naive 56 F II NA Breast CGPLBR57 Breast Cancer cfDNA Preoperative treatment naive 54 F III T2N2M0 Left Breast CGPLBR59 Breast Cancer cfDNA Preoperative treatment naive 42 F I T1N0M0 Left Breast CGPLBR60 Breast Cancer cfDNA Preoperative treatment naive 61 F II NA Left Breast CGPLBR61 Breast Cancer cfDNA Preoperative treatment naive 67 F II T2N1M0 Left Breast CGPLBR63 Breast Cancer cfDNA Preoperative treatment naive 48 F II T2N1M0 Left Breast CGPLBR65 Breast Cancer cfDNA Preoperative treatment naive 50 F II NA Left Breast CGPLBR68 Breast Cancer cfDNA Preoperative treatment naive 64 F III T4N1M0 Breast CGPLBR69 Breast Cancer cfDNA Preoperative treatment naive 43 F II T2N0M0 Breast CGPLBR70 Breast Cancer cfDNA Preoperative treatment naive 60 F II T2N1M0 Breast CGPLBR71 Breast Cancer cfDNA Preoperative treatment naive 65 F II T2N0M0 Breast CGPLBR72 Breast Cancer cfDNA Preoperative treatment naive

7 F II T2N0M0 Breast CGPLBR73 Breast Cancer cfDNA Preoperative treatment naive 60 F II T2N1M0 Breast CGPLBR76 Breast Cancer cfDNA Preoperative treatment naive 53 F II T2N0M0 Right Breast CGPLBR81 Breast Cancer cfDNA Preoperative treatment naive 54 F II NA Breast CGPLBR82 Breast Cancer cfDNA Preoperative treatment naive 70 F I T1N0M0 Right Breast CGPLBR83 Breast Cancer cfDNA Preoperative treatment naive 53 F II T2N1M0 Right Breast CGPLBR84 Breast Cancer cfDNA Preoperative treatment naive NA F III NA Breast CGPLBR87 Breast Cancer cfDNA Preoperative treatment naive 80 F II T2N1M0 Right Breast CGPLBR88 Breast Cancer cfDNA Preoperative treatment naive 48 F II T1N1M0 Left Breast CGPLBR90 Breast Cancer cfDNA Preoperative treatment naive 51 F II NA Right Breast CGPLBR91 Breast Cancer cfDNA Preoperative treatment naive 62 F III T2N2M0 Breast CGPLBR92 Breast Cancer cfDNA Preoperative treatment naive 58 F II T2N1M0 Breast CGPLBR93 Breast Cancer cfDNA Preoperative treatment naive 59 F II T1N0M0 Breast CGPLH189 Healthy cfDNA Preoperative treatment naive 74 M NA NA NA CGPLH190 Healthy cfDNA Preoperative treatment naive 67 M NA NA NA CGPLH192 Healthy cfDNA Preoperative treatment naive 74 M NA NA NA CGPLH193 Healthy cfDNA Preoperative treatment naive 72 F NA NA NA CGPLH194 Healthy cfDNA Preoperative treatment naive 75 F NA NA NA CGPLH196 Healthy cfDNA Preoperative treatment naive 64 M NA NA NA CGPLH197 Healthy cfDNA Preoperative treatment naive 74 M NA NA NA CGPLH198 Healthy cfDNA Preoperative treatment naive 66 M NA NA NA CGPLH199 Healthy cfDNA Preoperative treatment naive 75 F NA NA NA CGPLH200 Healthy cfDNA Preoperative treatment naive 51 M NA NA NA CGPLH201 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH202 Healthy cfDNA Preoperative treatment naive 73 M NA NA NA CGPLH203 Healthy cfDNA Preoperative treatment naive 59 M NA NA NA CGPLH205 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH208 Healthy cfDNA Preoperative treatment naive 75 F NA NA NA CGPLH209 Healthy cfDNA Preoperative treatment naive 74 M NA NA NA CGPLH210 Healthy cfDNA Preoperative treatment naive 75 M NA NA NA CGPLH211 Healthy cfDNA Preoperative treatment naive 75 F NA NA NA CGPLH300 Healthy cfDNA Preoperative treatment naive 72 F NA NA NA CGPLH307 Healthy cfDNA Preoperative treatment naive 5

M NA NA NA CGPLH308 Healthy cfDNA Preoperative treatment naive 60 M NA NA NA CGPLH309 Healthy cfDNA Preoperative treatment naive 61 F NA NA NA CGPLH310 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH311 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH314 Healthy cfDNA Preoperative treatment naive 5

M NA NA NA CGPLH314 Healthy cfDNA, Preoperative treatment naive 59 M NA NA NA tec

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icate CGPLH315 Healthy cfDNA Preoperative treatment naive 59 F NA NA NA CGPLH316 Healthy cfDNA Preoperative treatment naive 64 M NA NA NA CGPLH317 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH319 Healthy cfDNA Preoperative treatment naive 60 F NA NA NA CGPLH320 Healthy cfDNA Preoperative treatment naive 75 F NA NA NA CGPLH322 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH324 Healthy cfDNA Preoperative treatment naive 59 F NA NA NA CGPLH325 Healthy cfDNA Preoperative treatment naive 54 M NA NA NA CGPLH326 Healthy cfDNA Preoperative treatment naive 67 F NA NA NA CGPLH327 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH328 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH328 Healthy cfDNA, Preoperative treatment naive 68 F NA NA NA tec

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icate CGPLH329 Healthy cfDNA Preoperative treatment naive 59 M NA NA NA CGPLH330 Healthy cfDNA Preoperative treatment naive 75 M NA NA NA CGPLH331 Healthy cfDNA Preoperative treatment naive 55 M NA NA NA CGPLH331 Healthy cfDNA, Preoperative treatment naive 55 M NA NA NA tec

nical rep

icate CGPLH333 Healthy cfDNA Preoperative treatment naive 60 M NA NA NA CGPLH335 Healthy cfDNA Preoperative treatment naive 74 M NA NA NA CGPLH336 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH337 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH338 Healthy cfDNA Preoperative treatment naive 75 M NA NA NA CGPLH339 Healthy cfDNA Preoperative treatment naive 70 M NA NA NA CGPLH340 Healthy cfDNA Preoperative treatment naive 62 M NA NA NA CGPLH341 Healthy cfDNA Preoperative treatment naive 61 F NA NA NA CGPLH342 Healthy cfDNA Preoperative treatment naive 49 F NA NA NA CGPLH343 Healthy cfDNA Preoperative treatment naive 58 M NA NA NA CGPLH344 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH345 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH346 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH35 Healthy cfDNA Preoperative treatment naive 48 F NA NA NA CGPLH350 Healthy cfDNA Preoperative treatment naive 65 M NA NA NA CGPLH351 Healthy cfDNA Preoperative treatment naive 71 M NA NA NA CGPLH352 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH353 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH354 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH355 Healthy cfDNA Preoperative treatment naive 70 M NA NA NA CGPLH356 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH357 Healthy cfDNA Preoperative treatment naive 52 F NA NA NA CGPLH358 Healthy cfDNA Preoperative treatment naive 55 M NA NA NA CGPLH36 Healthy cfDNA Preoperative treatment naive 36 F NA NA NA CGPLH360 Healthy cfDNA Preoperative treatment naive 60 M NA NA NA CGPLH361 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH362 Healthy cfDNA Preoperative treatment naive 72 F NA NA NA CGPLH363 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH364 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH365 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH366 Healthy cfDNA Preoperative treatment naive 61 M NA NA NA CGPLH367 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH368 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH369 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH369 Healthy cfDNA, Preoperative treatment naive 55 F NA NA NA tec

nical rep

icate CGPLH37 Healthy cfDNA Preoperative treatment naive 39 F NA NA NA CGPLH370 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH371 Healthy cfDNA Preoperative treatment naive 57 F NA NA NA CGPLH380 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH381 Healthy cfDNA Preoperative treatment naive 56 F NA NA NA CGPLH382 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH383 Healthy cfDNA Preoperative treatment naive 62 F NA NA NA CGPLH384 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH385 Healthy cfDNA Preoperative treatment naive 69 M NA NA NA CGPLH386 Healthy cfDNA Preoperative treatment naive 62 M NA NA NA CGPLH386 Healthy cfDNA Preoperative treatment naive 62 M NA NA NA tec

nical rep

icate CGPLH387 Healthy cfDNA Preoperative treatment naive 71 F NA NA NA CGPLH388 Healthy cfDNA Preoperative treatment naive 57 F NA NA NA CGPLH389 Healthy cfDNA Preoperative treatment naive 73 F NA NA NA CGPLH390 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH391 Healthy cfDNA Preoperative treatment naive 58 M NA NA NA CGPLH391 Healthy cfDNA Preoperative treatment naive 58 M NA NA NA tec

nical rep

icate CGPLH392 Healthy cfDNA Preoperative treatment naive 57 F NA NA NA CGPLH393 Healthy cfDNA Preoperative treatment naive 54 M NA NA NA CGPLH394 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH395 Healthy cfDNA Preoperative treatment naive 56 F NA NA NA CGPLH395 Healthy cfDNA Preoperative treatment naive 56 F NA NA NA tec

nical rep

icate CGPLH396 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH398 Healthy cfDNA Preoperative treatment naive 68 M NA NA NA CGPLH399 Healthy cfDNA Preoperative treatment naive 62 F NA NA NA CGPLH400 Healthy cfDNA Preoperative treatment naive 64 M NA NA NA CGPLH400 Healthy cfDNA Preoperative treatment naive 64 M NA NA NA tec

nical rep

icate CGPLH401 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH401 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA tec

nical rep

icate CGPLH402 Healthy cfDNA Preoperative treatment naive 57 F NA NA NA CGPLH403 Healthy cfDNA Preoperative treatment naive 64 M NA NA NA CGPLH404 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH405 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH406 Healthy cfDNA Preoperative treatment naive 57 M NA NA NA CGPLH407 Healthy cfDNA Preoperative treatment naive 75 F NA NA NA CGPLH408 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH409 Healthy cfDNA Preoperative treatment naive 5

M NA NA NA CGPLH410 Healthy cfDNA Preoperative treatment naive 52 M NA NA NA CGPLH411 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH412 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH413 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH414 Healthy cfDNA Preoperative treatment naive 56 M NA NA NA CGPLH415 Healthy cfDNA Preoperative treatment naive 59 M NA NA NA CGPLH416 Healthy cfDNA Preoperative treatment naive 58 F NA NA NA CGPLH417 Healthy cfDNA Preoperative treatment naive 70 M NA NA NA CGPLH418 Healthy cfDNA Preoperative treatment naive 70 F NA NA NA CGPLH419 Healthy cfDNA Preoperative treatment naive 65 F NA NA NA CGPLH42 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH420 Healthy cfDNA Preoperative treatment naive 51 M NA NA NA CGPLH422 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH423 Healthy cfDNA Preoperative treatment naive 54 M NA NA NA CGPLH424 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH425 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH426 Healthy cfDNA Preoperative treatment naive 68 M NA NA NA CGPLH427 Healthy cfDNA Preoperative treatment naive 68 M NA NA NA CGPLH428 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH429 Healthy cfDNA Preoperative treatment naive 63 F NA NA NA CGPLH43 Healthy cfDNA Preoperative treatment naive 4

F NA NA NA CGPLH430 Healthy cfDNA Preoperative treatment naive

9 F NA NA NA CGPLH431 Healthy cfDNA Preoperative treatment naive 59 F NA NA NA CGPLH432 Healthy cfDNA Preoperative treatment naive 59 F NA NA NA CGPLH434 Healthy cfDNA Preoperative treatment naive 59 M NA NA NA CGPLH435 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH436 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH437 Healthy cfDNA Preoperative treatment naive 56 M NA NA NA CGPLH438 Healthy cfDNA Preoperative treatment naive 69 M NA NA NA CGPLH439 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH440 Healthy cfDNA Preoperative treatment naive 72 M NA NA NA CGPLH441 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH442 Healthy cfDNA Preoperative treatment naive 59 F NA NA NA CGPLH443 Healthy cfDNA Preoperative treatment naive 52 F NA NA NA CGPLH444 Healthy cfDNA Preoperative treatment naive 60 F NA NA NA CGPLH445 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH446 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH447 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH448 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH449 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH45 Healthy cfDNA Preoperative treatment naive 58 F NA NA NA CGPLH450 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH451 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH452 Healthy cfDNA Preoperative treatment naive 69 M NA NA NA CGPLH453 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH455 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH455 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA tec

nical rep

icate CGPLH456 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH457 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH458 Healthy cfDNA Preoperative treatment naive 52 F NA NA NA CGPLH459 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH46 Healthy cfDNA Preoperative treatment naive 35 F NA NA NA CGPLH460 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH463 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH464 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH465 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH466 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH466 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA tec

nical rep

icate CGPLH467 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH468 Healthy cfDNA Preoperative treatment naive 53 M NA NA NA CGPLH469 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH47 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH470 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH471 Healthy cfDNA Preoperative treatment naive 70 F NA NA NA CGPLH472 Healthy cfDNA Preoperative treatment naive 69 F NA NA NA CGPLH473 Healthy cfDNA Preoperative treatment naive 62 M NA NA NA CGPLH474 Healthy cfDNA Preoperative treatment naive 63 M NA NA NA CGPLH475 Healthy cfDNA Preoperative treatment naive 67 F NA NA NA CGPLH476 Healthy cfDNA Preoperative treatment naive 65 F NA NA NA CGPLH477 Healthy cfDNA Preoperative treatment naive 61 F NA NA NA CGPLH478 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH479 Healthy cfDNA Preoperative treatment naive 52 M NA NA NA CGPLH48 Healthy cfDNA Preoperative treatment naive 38 F NA NA NA CGPLH480 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH481 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH482 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH483 Healthy cfDNA Preoperative treatment naive 66 M NA NA NA CGPLH484 Healthy cfDNA Preoperative treatment naive 72 M NA NA NA CGPLH485 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH486 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH487 Healthy cfDNA Preoperative treatment naive 50 M NA NA NA CGPLH488 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH49 Healthy cfDNA Preoperative treatment naive 39 F NA NA NA CGPLH490 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH491 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH492 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH493 Healthy cfDNA Preoperative treatment naive 64 M NA NA NA CGPLH494 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH495 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH496 Healthy cfDNA Preoperative treatment naive 74 M NA NA NA CGPLH497 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH497 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA tec

nical rep

icate CGPLH498 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH499 Healthy cfDNA Preoperative treatment naive 52 F NA NA NA CGPLH50 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH500 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH501 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH502 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH503 Healthy cfDNA Preoperative treatment naive 67 M NA NA NA CGPLH504 Healthy cfDNA Preoperative treatment naive 57 F NA NA NA CGPLH504 Healthy cfDNA Preoperative treatment naive 57 F NA NA NA tec

nical rep

icate CGPLH505 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH506 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH507 Healthy cfDNA Preoperative treatment naive 56 M NA NA NA CGPLH508 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH508 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA tec

nical rep

icate CGPLH509 Healthy cfDNA Preoperative treatment naive 60 M NA NA NA CGPLH51 Healthy cfDNA Preoperative treatment naive 48 F NA NA NA CGPLH510 Healthy cfDNA Preoperative treatment naive 67 M NA NA NA CGPLH511 Healthy cfDNA Preoperative treatment naive 75 M NA NA NA CGPLH512 Healthy cfDNA Preoperative treatment naive 52 M NA NA NA CGPLH513 Healthy cfDNA Preoperative treatment naive 57 M NA NA NA CGPLH514 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH515 Healthy cfDNA Preoperative treatment naive 68 F NA NA NA CGPLH516 Healthy cfDNA Preoperative treatment naive 65 F NA NA NA CGPLH517 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH517 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA tec

nical rep

icate CGPLH518 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH519 Healthy cfDNA Preoperative treatment naive 54 M NA NA NA CGPLH522 Healthy cfDNA Preoperative treatment naive 40 F NA NA NA CGPLH520 Healthy cfDNA Preoperative treatment naive 51 F NA NA NA CGPLH54 Healthy cfDNA Preoperative treatment naive 47 F NA NA NA CGPLH55 Healthy cfDNA Preoperative treatment naive 46 F NA NA NA CGPLH56 Healthy cfDNA Preoperative treatment naive 42 F NA NA NA CGPLH57 Healthy cfDNA Preoperative treatment naive 39 F NA NA NA CGPLH59 Healthy cfDNA Preoperative treatment naive 34 F NA NA NA CGPLH625 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH625 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH626 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA tec

nical rep

icate CGPLH63 Healthy cfDNA Preoperative treatment naive 47 F NA NA NA CGPLH639 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH64 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH640 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH642 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH643 Healthy cfDNA Preoperative treatment naive 55 F NA NA NA CGPLH644 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH646 Healthy cfDNA Preoperative treatment naive 50 F NA NA NA CGPLH75 Healthy cfDNA Preoperative treatment naive 46 F NA NA NA CGPLH76 Healthy cfDNA Preoperative treatment naive 53 F NA NA NA CGPLH77 Healthy cfDNA Preoperative treatment naive 46 F NA NA NA CGPLH78 Healthy cfDNA Preoperative treatment naive 34 F NA NA NA CGPLH79 Healthy cfDNA Preoperative treatment naive 37 F NA NA NA CGPLH80 Healthy cfDNA Preoperative treatment naive 37 F NA NA NA CGPLH81 Healthy cfDNA Preoperative treatment naive 54 F NA NA NA CGPLH82 Healthy cfDNA Preoperative treatment naive 38 F NA NA NA CGPLH83 Healthy cfDNA Preoperative treatment naive 60 F NA NA NA CGPLH84 Healthy cfDNA Preoperative treatment naive 45 F NA NA NA CGPLLU13 Lung Cancer cfDNA Pre treatment, Day 2 72 F IV T1BN2bM1a Right Lung CGPLLU13 Lung Cancer cfDNA Post-treatment, Day 5 72 F IV T1BN2bM1a Right Lung CGPLLU13 Lung Cancer cfDNA Post-treatment, Day 28 72 F IV T1BN2bM1a Right Lung CGPLLU13 Lung Cancer cfDNA Post-treatment, Day

1 72 F IV T1BN2bM1a Right Lung CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day −38 55 F IV T1N1M0 Right Lower Lobe of Lung CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day −16 55 F IV T1N1M0 Right Lower Lobe of Lung CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day −3 55 F IV T1N1M0 Right Lower Lobe of Lung CGPLLU14 Lung Cancer cfDNA Pre-treatment, Day 0 55 F IV T1N1M0 Right Lower Lobe of Lung CGPLLU14 Lung Cancer cfDNA Post-treatment, Day 0.33 55 F IV T1N1M0 Right Lower Lobe of Lung CGPLLU14 Lung Cancer cfDNA Post-treatment, Day 7 55 F IV T1N1M0 Right Lower Lobe of Lung CGPLLU144 Lung Cancer cfDNA Preoperative treatment naive 52 M II T2aN1M0 Lung CGPLLU147 Lung Cancer cfDNA Preoperative treatment naive 60 M III T3N2M0 Lung CGPLLU161 Lung Cancer cfDNA Preoperative treatment naive 41 F II T3N0M0 Lung CGPLLU162 Lung Cancer cfDNA Preoperative treatment naive 38 M II T1N1M0 Right Lung CGPLLU163 Lung Cancer cfDNA Preoperative treatment naive 66 M II T1N1M0 Left Lung CGPLLU165 Lung Cancer cfDNA Preoperative treatment naive 68 F II T1N1M0 Right Lung CGPLLU168 Lung Cancer cfDNA Preoperative treatment naive 70 F I T2aN0M0 Lung CGPLLU169 Lung Cancer cfDNA Preoperative treatment naive 64 M I T1bN0M0 Lung CGPLLU175 Lung Cancer cfDNA Preoperative treatment naive 47 M I T2N0M0 Lung CGPLLU176 Lung Cancer cfDNA Preoperative treatment naive 58 M I T2N0M0 Lung CGPLLU177 Lung Cancer cfDNA Preoperative treatment naive 45 M II T3N0M0 Right Lung CGPLLU180 Lung Cancer cfDNA Preoperative treatment naive 57 M I T2N0M0 Right Lung CGPLLU198 Lung Cancer cfDNA Preoperative treatment naive 49 F I T2N0M0 Left Lung CGPLLU202 Lung Cancer cfDNA Preoperative treatment naive 68 M I T2N0M0 Right Lung CGPLLU203 Lung Cancer cfDNA Preoperative treatment naive 66 M II T3N0M0 Right Lung CGPLLU205 Lung Cancer cfDNA Preoperative treatment naive 65 M II T3N0M0 Left Lung CGPLLU206 Lung Cancer cfDNA Preoperative treatment naive 55 M III T3N1M0 Right Lung CGPLLU207 Lung Cancer cfDNA Preoperative treatment naive 60 F II T2N1M0 Lung CGPLLU208 Lung Cancer cfDNA Preoperative treatment naive 56 F II T2N1M0 Lung CGPLLU209 Lung Cancer cfDNA Preoperative treatment naive 65 M II T2aN0M0 Lung CGPLLU244 Lung Cancer cfDNA Pre-treatment, Day −7 66 F IV NA Right Upper Lobe of Lung CGPLLU244 Lung Cancer cfDNA Pre-treatment, Day −1 66 F IV NA Right Upper Lobe of Lung CGPLLU244 Lung Cancer cfDNA Post-treatment, Day

66 F IV NA Right Upper Lobe of Lung CGPLLU244 Lung Cancer cfDNA Post-treatment, Day

2 66 F IV NA Right Upper Lobe of Lung CGPLLU245 Lung Cancer cfDNA Pre-treatment, Day 32 49 M IV T2aN2M1B Left Upper Lobe of Lung CGPLLU245 Lung Cancer cfDNA Pre-treatment, Day 0 49 M IV T2aN2M1B Left Upper Lobe of Lung CGPLLU245 Lung Cancer cfDNA Post-treatment, Day 7 49 M IV T2aN2M1B Left Upper Lobe of Lung CGPLLU245 Lung Cancer cfDNA Post-treatment, Day 21 49 M IV T2aN2M1B Left Upper Lobe of Lung CGPLLU246 Lung Cancer cfDNA Pre-treatment, Day −21 65 F IV NA Right Lower Lobe of Lung CGPLLU246 Lung Cancer cfDNA Pre-treatment, Day 0 65 F IV NA Right Lower Lobe of Lung CGPLLU246 Lung Cancer cfDNA Post-treatment, Day 9

5 F IV NA Right Lower Lobe of Lung CGPLLU246 Lung Cancer cfDNA Post-treatment, Day

2 65 F IV NA Right Lower Lobe of Lung CGPLLU264 Lung Cancer cfDNA Pre-treatment, Day −1 84 M IV T1N2BM1 Left Middle Lung CGPLLU264 Lung Cancer cfDNA Post-treatment, Day

84 M IV T1N2BM1 Left Middle Lung CGPLLU264 Lung Cancer cfDNA Post-treatment, Day 27 84 M IV T1N2BM1 Left Middle Lung CGPLLU264 Lung Cancer cfDNA Post-treatment, Day

9 84 M IV T1N2BM1 Left Middle Lung CGPLLU265 Lung Cancer cfDNA Pre-treatment, Day 0 71 F IV T1N0Mx Left Lower Lobe of Lung CGPLLU265 Lung Cancer cfDNA Post-treatment, Day 3 71 F IV T1N0Mx Left Lower Lobe of Lung CGPLLU265 Lung Cancer cfDNA Post-treatment, Day 7 71 F IV T1N0Mx Left Lower Lobe of Lung CGPLLU265 Lung Cancer cfDNA Post-treatment, Day 84 71 F IV T1N0Mx Left Lower Lobe of Lung CGPLLU266 Lung Cancer cfDNA Pre-treatment, Day 0 78 M IV T2aN1 Left Lower Lobe of Lung CGPLLU266 Lung Cancer cfDNA Post-treatment, Day 16 78 M IV T2aN1 Left Lower Lobe of Lung CGPLLU266 Lung Cancer cfDNA Post-treatment, Day

3 78 M IV T2aN1 Left Lower Lobe of Lung CGPLLU266 Lung Cancer cfDNA Post-treatment, Day 328 78 M IV T2aN1 Left Lower Lobe of Lung CGPLLU267 Lung Cancer cfDNA Pre-treatment, Day −1 55 F IV T3NxM1a Right Upper Lobe of Lung CGPLLU267 Lung Cancer cfDNA Post-treatment, Day 34 55 F IV T3NxM1a Right Upper Lobe of Lung CGPLLU267 Lung Cancer cfDNA Post-treatment, Day

0 55 F IV T3NxM1a Right Upper Lobe of Lung CGPLLU269 Lung Cancer cfDNA Pre-treatment, Day 0 52 F IV T1CNxM1C Right Paratracheal Lesion CGPLLU269 Lung Cancer cfDNA Post-treatment, Day 9 52 F IV T1CNxM1C Right Paratracheal Lesion CGPLLU269 Lung Cancer cfDNA Post-treatment, Day 2

52 F IV T1CNxM1C Right Paratracheal Lesion CGPLLU271 Lung Cancer cfDNA Post-treatment, Day 259 73 M IV T1aNxM1 Left Upper Lobe of Lung CGPLLU271 Lung Cancer cfDNA Pre-treatment, Day 0 73 M IV T1aNxM1 Left Upper Lobe of Lung CGPLLU271 Lung Cancer cfDNA Post-treatment, Day

73 M IV T1aNxM1 Left Upper Lobe of Lung CGPLLU271 Lung Cancer cfDNA Post-treatment, Day 20 73 M IV T1aNxM1 Left Upper Lobe of Lung CGPLLU271 Lung Cancer cfDNA Post-treatment, Day 104 73 M IV T1aNxM1 Left Upper Lobe of Lung CGPLLU43 Lung Cancer cfDNA Pre-treatment, Day −1 57 F IV T1BN0M0 Right Lower Lobe of Lung CGPLLU43 Lung Cancer cfDNA Post-treatment, Day 6 57 F IV T1BN0M0 Right Lower Lobe of Lung CGPLLU43 Lung Cancer cfDNA Post-treatment, Day 27 57 F IV T1BN0M0 Right Lower Lobe of Lung CGPLLU43 Lung Cancer cfDNA Post-treatment, Day

3 57 F IV T1BN0M0 Right Lower Lobe of Lung CGPLLU86 Lung Cancer cfDNA Pre-treatment, Day 0 55 M IV NA Left Upper Lobe of Lung CGPLLU86 Lung Cancer cfDNA Post-treatment, Day 0.5 55 M IV NA Left Upper Lobe of Lung CGPLLU86 Lung Cancer cfDNA Post-treatment, Day 7 55 M IV NA Left Upper Lobe of Lung CGPLLU86 Lung Cancer cfDNA Post-treatment, Day 17 55 M IV NA Left Upper Lobe of Lung CGPLLU88 Lung Cancer cfDNA Pre-treatment, Day 0 59 M IV NA Right Middle Lobe of Lung CGPLLU88 Lung Cancer cfDNA Post-treatment, Day 7 59 M IV NA Right Middle Lobe of Lung CGPLLU88 Lung Cancer cfDNA Post-treatment, Day 297 59 M IV NA Right Middle Lobe of Lung CGPLLU89 Lung Cancer cfDNA Pre-treatment, Day 0 54 F IV NA Right Upper Lobe of Lung CGPLLU89 Lung Cancer cfDNA Post-treatment, Day 7 54 F IV NA Right Upper Lobe of Lung CGPLLU89 Lung Cancer cfDNA Post-treatment, Day 22 54 F IV NA Right Upper Lobe of Lung CGPLOV11 Ovarian Cancer cfDNA Preoperative treatment naive 51 F IV T3cN0M1 Right Ovary CGPLOV12 Ovarian Cancer cfDNA Preoperative treatment naive 45 F I T1aN0MX Ovary CGPLOV13 Ovarian Cancer cfDNA Preoperative treatment naive

2 F IV T1bN0M1 Right Ovary CGPLOV15 Ovarian Cancer cfDNA Preoperative treatment naive 54 F III T3N1M0 Ovary CGPLOV16 Ovarian Cancer cfDNA Preoperative treatment naive 40 F III T3aN0M0 Ovary CGPLOV19 Ovarian Cancer cfDNA Preoperative treatment naive 52 F II T2aN0M0 Ovary CGPLOV20 Ovarian Cancer cfDNA Preoperative treatment naive 52 F II T2aN0M0 Left Ovary CGPLOV21 Ovarian Cancer cfDNA Preoperative treatment naive 51 M IV TanyN1M1 Ovary CGPLOV22 Ovarian Cancer cfDNA Preoperative treatment naive 54 F III T1cNXMX Left Ovary CGPLOV23 Ovarian Cancer cfDNA Preoperative treatment naive 47 F I T1aN0M0 Ovary CGPLOV24 Ovarian Cancer cfDNA Preoperative treatment naive 14 F I T1aN0M0 Ovary CGPLOV25 Ovarian Cancer cfDNA Preoperative treatment naive 18 F I T1aN0M0 Ovary CGPLOV26 Ovarian Cancer cfDNA Preoperative treatment naive 35 F I T1aN0M0 Ovary CGPLOV28 Ovarian Cancer cfDNA Preoperative treatment naive 63 F I T1aNxM0 Right Ovary CGPLOV31 Ovarian Cancer cfDNA Preoperative treatment naive 45 F III T3aNxM0 Right Ovary CGPLOV32 Ovarian Cancer cfDNA Preoperative treatment naive 53 F I T1aNxM0 Left Ovary CGPLOV37 Ovarian Cancer cfDNA Preoperative treatment naive 40 F I T1cN0M0 Ovary CGPLOV38 Ovarian Cancer cfDNA Preoperative treatment naive 46 F I T1cN0M0 Ovary CGPLOV40 Ovarian Cancer cfDNA Preoperative treatment naive 53 F IV T3aN0M0 Ovary CGPLOV41 Ovarian Cancer cfDNA Preoperative treatment naive 57 F IV T3N0M1 Ovary CGPLOV42 Ovarian Cancer cfDNA Preoperative treatment naive 52 F I T3N0M1 Ovary CGPLOV43 Ovarian Cancer cfDNA Preoperative treatment naive 30 F I T3aN0M0 Ovary CGPLOV44 Ovarian Cancer cfDNA Preoperative treatment naive 69 F I T1aN0M0 Ovary CGPLOV46 Ovarian Cancer cfDNA Preoperative treatment naive 58 F I T1bN0M0 Ovary CGPLOV47 Ovarian Cancer cfDNA Preoperative treatment naive 41 F I T1aN0M0 Ovary CGPLOV48 Ovarian Cancer cfDNA Preoperative treatment naive 52 F I T1bN0M0 Ovary CGPLOV49 Ovarian Cancer cfDNA Preoperative treatment naive 68 F III T3bN0M0 Ovary CGPLOV50 Ovarian Cancer cfDNA Preoperative treatment naive 30 F III T3

N0M0 Ovary CGPLPA112 Pancreatic Cancer cfDNA Preoperative treatment naive 58 M II NA Intra Pancreatic Bile Duct CGPLPA113 Duodenal Cancer cfDNA Preoperative treatment naive 71 M I NA Intra Pancreatic Bile Duct CGPLPA114 Bile Duct Cancer cfDNA Preoperative treatment naive NA F II NA Intra Pancreatic Bile Duct CGPLPA115 Bile Duct Cancer cfDNA Preoperative treatment naive NA M IV NA Intra Hepatic Bi

e Duct CGPLPA117 Bile Duct Cancer cfDNA Preoperative treatment naive NA M II NA Intra Pancreatic Bile Duct CGPLPA118 Bile Duct Cancer cfDNA Preoperative treatment naive 68 F I NA Bile Duct CGPLPA122 Bile Duct Cancer cfDNA Preoperative treatment naive 62 F II NA Bile Duct CGPLPA124 Bile Duct Cancer cfDNA Preoperative treatment naive 83 F II NA Bile Duct CGPLPA125 Bile Duct Cancer cfDNA Preoperative treatment naive 58 M II NA Bile Duct CGPLPA126 Bile Duct Cancer cfDNA Preoperative treatment naive 68 M II NA Bile Duct CGPLPA127 Bile Duct Cancer cfDNA Preoperative treatment naive 71 F IV NA Bile Duct CGPLPA128 Bile Duct Cancer cfDNA Preoperative treatment naive 67 M II NA Bile Duct CGPLPA129 Bile Duct Cancer cfDNA Preoperative treatment naive 56 F II NA Bile Duct CGPLPA130 Bile Duct Cancer cfDNA Preoperative treatment naive 82 F II NA Bile Duct CGPLPA131 Bile Duct Cancer cfDNA Preoperative treatment naive 71 M II NA Bile Duct CGPLPA134 Bile Duct Cancer cfDNA Preoperative treatment naive 68 M II NA Bile Duct CGPLPA135 Bile Duct Cancer cfDNA Preoperative treatment naive 67 F I NA Bile Duct CGPLPA136 Bile Duct Cancer cfDNA Preoperative treatment naive 69 F II NA Bile Duct CGPLPA137 Bile Duct Cancer cfDNA Preoperative treatment naive NA M II NA Bile Duct CGPLPA139 Bile Duct Cancer cfDNA Preoperative treatment naive NA M IV NA Bile Duct CGPLPA14 Pancreatic Cancer cfDNA Preoperative treatment naive 68 M II NA Pancreas CGPLPA140 Bile Duct Cancer cfDNA Preoperative treatment naive 52 M II NA Extra Hepatic Bile Duct CGPLPA141 Bile Duct Cancer cfDNA Preoperative treatment naive 68 F II NA Extra Hepatic Bile Duct CGPLPA15 Pancreatic Cancer cfDNA Preoperative treatment naive 70 F II NA Pancreas CGPLPA155 Bile Duct Cancer cfDNA Preoperative treatment naive NA F II NA NA CGPLPA156 Pancreatic Cancer cfDNA Preoperative treatment naive 73 F II NA Pancreas CGPLPA165 Bile Duct Cancer cfDNA Preoperative treatment naive 42 M I NA Bile Duct CGPLPA168 Bile Duct Cancer cfDNA Preoperative treatment naive 58 M II NA Bile Duct CGPLPA17 Pancreatic Cancer cfDNA Preoperative treatment naive 65 M II NA Pancreas CGPLPA184 Bile Duct Cancer cfDNA Preoperative treatment naive 75 F II NA Bile Duct CGPLPA187 Bile Duct Cancer cfDNA Preoperative treatment naive 67 F II NA Bile Duct CGPLPA23 Pancreatic Cancer cfDNA Preoperative treatment naive 58 F II NA Pancreas CGPLPA25 Pancreatic Cancer cfDNA Preoperative treatment naive 65 F II NA Pancreas CGPLPA26 Pancreatic Cancer cfDNA Preoperative treatment naive 64 M II NA Pancreas CGPLPA28 Pancreatic Cancer cfDNA Preoperative treatment naive 79 F II NA Pancreas CGPLPA33 Pancreatic Cancer cfDNA Preoperative treatment naive 67 F II NA Pancreas CGPLPA34 Pancreatic Cancer cfDNA Preoperative treatment naive 7

M II NA Pancreas CGPLPA37 Pancreatic Cancer cfDNA Preoperative treatment naive 67 F II NA Pancreas CGPLPA38 Pancreatic Cancer cfDNA Preoperative treatment naive 65 M II NA Pancreas CGPLPA39 Pancreatic Cancer cfDNA Preoperative treatment naive 67 F II NA Pancreas CGPLPA40 Pancreatic Cancer cfDNA Preoperative treatment naive 64 M II NA Pancreas CGPLPA42 Pancreatic Cancer cfDNA Preoperative treatment naive 73 M II NA Pancreas CGPLPA46 Pancreatic Cancer cfDNA Preoperative treatment naive 59 F II NA Pancreas CGPLPA47 Pancreatic Cancer cfDNA Preoperative treatment naive 67 M II NA Pancreas CGPLPA48 Pancreatic Cancer cfDNA Preoperative treatment naive 72 F I NA Pancreas CGPLPA52 Pancreatic Cancer cfDNA Preoperative treatment naive 63 M II NA Pancreas CGPLPA53 Pancreatic Cancer cfDNA Preoperative treatment naive 46 M I NA Pancreas CGPLPA58 Pancreatic Cancer cfDNA Preoperative treatment naive 74 F II NA Pancreas CGPLPA59 Pancreatic Cancer cfDNA Preoperative treatment naive 5

F II NA Pancreas CGPLPA67 Pancreatic Cancer cfDNA Preoperative treatment naive 55 M III NA Pancreas CGPLPA69 Pancreatic Cancer cfDNA Preoperative treatment naive 70 M I NA Pancreas CGPLPA71 Pancreatic Cancer cfDNA Preoperative treatment naive 64 M II NA Pancreas CGPLPA74 Pancreatic Cancer cfDNA Preoperative treatment naive 71 F II NA Pancreas CGPLPA76 Pancreatic Cancer cfDNA Preoperative treatment naive 69 M II NA Pancreas CGPLPA85 Pancreatic Cancer cfDNA Preoperative treatment naive 77 F II NA Pancreas CGPLPA86 Pancreatic Cancer cfDNA Preoperative treatment naive 66 M II NA Pancreas CGPLPA92 Pancreatic Cancer cfDNA Preoperative treatment naive 72 M II NA Pancreas CGPLPA93 Pancreatic Cancer cfDNA Preoperative treatment naive 48 M II NA Pancreas CGPLPA94 Pancreatic Cancer cfDNA Preoperative treatment naive 72 F II NA Pancreas CGPLPA95 Pancreatic Cancer cfDNA Preoperative treatment naive 64 F II NA Pancreas CGST102 Gastric cancer cfDNA Preoperative treatment naive 7

F II T3N0M0 Stomach CGST1

Gastric cancer cfDNA Preoperative treatment naive 49 M IV TXNXM1 Stomach CGST110 Gastric cancer cfDNA Preoperative treatment naive 77 M III T4AN3aM0 Stomach CGST114 Gastric cancer cfDNA Preoperative treatment naive 65 M III T4AN

M0 Stomach CGST13 Gastric cancer cfDNA Preoperative treatment naive 72 F II T1AN2M0 Stomach CGST131 Gastric cancer cfDNA Preoperative treatment naive 63 M III T2

3aM0 Stomach CGST141 Gastric cancer cfDNA Preoperative treatment naive 38 F III T3N2M0 Stomach CGST16 Gastric cancer cfDNA Preoperative treatment naive 78 M III T4AN3aM0 Stomach CGST18 Gastric cancer cfDNA Preoperative treatment naive 56 M II T3N0M0 Stomach CGST2

Gastric cancer cfDNA Preoperative treatment naive 39 M II T2N1 (mi)M0 Stomach CGST26 Gastric cancer cfDNA Preoperative treatment naive 51 M IV TXNXM1 Stomach CGST28 Gastric cancer cfDNA Preoperative treatment naive 55 M X TXNXM

Stomach CGST30 Gastric cancer cfDNA Preoperative treatment naive 64 F III T3N2M0 Stomach CGST32 Gastric cancer cfDNA Preoperative treatment naive 67 M II T3N1M0 Stomach CGST33 Gastric cancer cfDNA Preoperative treatment naive 61 M I T2N0M0 Stomach CGST38 Gastric cancer cfDNA Preoperative treatment naive 71 F 0 T0N0M0 Stomach CGST39 Gastric cancer cfDNA Preoperative treatment naive 51 M IV TXNXM

Stomach CGST4

Gastric cancer cfDNA Preoperative treatment naive 66 F IV TXNXM

Stomach CGST45 Gastric cancer cfDNA Preoperative treatment naive 41 F II T3N0M0 Stomach CGST47 Gastric cancer cfDNA Preoperative treatment naive 74 F I T1AN0M0 Stomach CGST48 Gastric cancer cfDNA Preoperative treatment naive 62 M IV TXNXM1 Stomach CGST53 Gastric cancer cfDNA Preoperative treatment naive 70 M 0 T0N0M0 Stomach CGST58 Gastric cancer cfDNA Preoperative treatment naive 58 M III T4AN3bM0 Stomach CGST67 Gastric cancer cfDNA Preoperative treatment naive 69 M I T1BN0M0 Stomach CGST77 Gastric cancer cfDNA Preoperative treatment naive 70 M IV TXNXM1 Stomach CGST80 Gastric cancer cfDNA Preoperative treatment naive 58 M III T3

3aM0 Stomach CGST8

Gastric cancer cfDNA Preoperative treatment naive 64 F I T2N0M

Stomach CGH14 Healthy

an adult NA NA M NA NA NA elutriated lymopho

CGH15 Healthy

an adult NA NA F NA NA NA elutriated lymopho

Whole Tar- Tar- Genome geted geted Volume cfDNA Fragment Fragment Muta- Degree of Location of of Ex- cfDNA Profile Profile tion Histopathological Differen- Metastases at Plasma tracted Input Anal- Anal- Anal- Patient Diagnosis tiation Diagnosis (ml) (ng/ml) (ng/ml) ysis ysis ysis CGCRC321 Adenocarcinoma Moderate Synchronous Liver 7.9 7.80 7.80 Y Y Y CGCRC232 Adenocarcinoma Moderate Synchronous 7.9 6.73 6.73 Y Y Y liver, Lung CGCRC29

Adenocarcinoma Moderate Synchronous Liver 7.2 3.83 3.83 Y Y Y CGCRC294 Adenocarcinoma Moderate None 8.4 18.87 18.87 Y Y Y CGCRC296 Adenocarcinoma Poor None 4.3 31.24 31.24 Y Y Y CGCRC299 Adenocarcinoma Moderate None 8.8 10.18 10.18 Y Y Y CGCRC3

0 Adenocarcinoma Moderate None 4.3 10.48 10.48 Y Y Y CGCRC3

1 Adenocarcinoma Moderate None 4.1 6.51 6.51 Y Y Y CGCRC3

2 Adenocarcinoma Moderate None 4.3 52.13 52.13 Y Y Y CGCRC3

4 Adenocarcinoma Moderate None 4.1 30.19 30.19 Y Y Y CGCRC3

5 Adenocarcinoma Moderate None 8.6 9.10 9.10 Y Y Y CGCRC3

6 Adenocarcinoma Moderate None 4.5 24.31 24.31 Y Y Y CGCRC3

7 Adenocarcinoma Moderate None 8.5 14.26 14.26 Y Y Y CGCRC3

Adenocarcinoma Moderate None 4.3 46.87 46.87 Y Y Y CGCRC311 Adenocarcinoma Moderate None 8.5 3.91 3.91 Y Y Y CGCRC315 Adenocarcinoma Moderate None 8.6 9.67 9.67 Y Y Y CGCRC316 Adenocarcinoma Moderate None 4.9 52.1

52.1

Y Y Y CGCRC317 Adenocarcinoma Moderate None 8.8 16.08 16.08 Y Y Y CGCRC318 Adenocarcinoma Moderate None 9.8 18.24 18.24 Y Y Y CGCRC319 Adenocarcinoma Moderate None 4.2 53.54 53.54 Y N Y CGCRC320 Adenocarcinoma Moderate None 4.5 30.37 30.37 Y Y Y CGCRC321 Adenocarcinoma Moderate None 9.

4.25 4.25 Y Y Y CGCRC333 Adenocarcinoma NA Liver 4.0 113.88 113.88 Y Y Y CGCRC336 Adenocarcinoma NA Liver 4.4 211.74 211.74 Y Y Y CGCRC338 Adenocarcinoma NA Liver 2.3 109.76 109.76 Y Y Y CGCRC341 Adenocarcinoma NA Liver 4.6 156.62 156.62 Y N Y CGCRC342 Adenocarcinoma NA Liver 3.9 56.09 56.09 Y N Y CGLU316 Adeno, Squamous, Poor Lung 5.0 2.38 2.38 Y N Y Small Cell Carcinoma CGLU316 Adeno, Squamous, Poor Lung 5.0 2.11 2.11 Y N Y Small Cell Carcinoma CGLU316 Adeno, Squamous, Poor Lung 5.0 0.87 1.07 Y N Y Small Cell Carcinoma CGLU316 Adeno, Squamous, Poor Lung 2.0 8.74 8.75 Y N Y Small Cell Carcinoma CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 34.77 25.00 Y N Y Peritoneum CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 15.63 15.64 Y N Y Peritoneum CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 9.22 9.22 Y N Y Peritoneum CGLU344 Adenocarcinoma NA Pleura, Liver, 5.0 5.31 5.32 Y N Y Peritoneum CGLU369 Adenocarcinoma NA Brain 2.0 11.28 11.28 Y N Y CGLU369 Adenocarcinoma NA Brain 5.0 10.09 10.09 Y N Y CGLU369 Adenocarcinoma NA Brain 5.0 6.69 6.70 Y N Y CGLU369 Adenocarcinoma NA Brain 5.0 8.41 8.42 Y N Y CGLU373 Adenocarcinoma Moderate None 5.0 6.35 6.35 Y N Y CGLU373 Adenocarcinoma Moderate None 5.0 6.28 6.28 Y N Y CGLU373 Adenocarcinoma Moderate None 5.0 3.82 3.82 Y N Y CGLU373 Adenocarcinoma Moderate None 3.5 5.55 5.55 Y N Y CGPLBR100 Infiltration Ductal Carcinoma NA None 4.0 4.25 4.25 Y N Y CGPLBR101 Infiltration Lobular Carcinoma Moderate None 4.0 37.88 37.88 Y N Y CGPLBR102 Infiltration Ductal Carcinoma Moderate None 3.6 13.

7 13.67 Y N Y CGPLBR103 Infiltration Ductal Carcinoma Moderate None 3.6 7.11 7.11 Y N Y CGPLBR104 Infiltration Lobular Carcinoma Moderate None 4.7 19.89 19.89 Y N Y CGPLBR12 Ductal Carcinoma insitu NA NA 4.3 4.21 4.21 Y N N with Microinvasion CGPLBR18 Infiltration Lobular Carcinoma NA NA 4.1 40.39 30.49 Y N N CGPLBR23 Infiltration Ductal Carcinoma NA None 4.7 20.09 20.09 Y N N CGPLBR24 Infiltration Ductal Carcinoma NA None 3.6 58.33 34.72 Y N N CGPLBR28 Infiltration Ductal Carcinoma NA None 4.2 12.86 12.86 Y N N CGPLBR30 Infiltration Ductal Carcinoma NA None 4.1 59.73 30.49 Y N N CGPLBR31 Infiltration Ductal Carcinoma NA None 3.4 23.94 23.94 Y N N CGPLBR32 Infiltration Ductal Carcinoma NA None 4.4 71.23 28.41 Y N N CGPLBR33 Infiltration Lobular Carcinoma NA None 4.4 11.00 11.00 Y N N CGPLBR34 Infiltration Lobular Carcinoma NA None 4.4 23.61 23.61 Y N N CGPLBR35 Ductal Carcinoma insitu NA None 4.5 22.58 22.58 Y N N with Microinvasion CGPLBR36 Infiltration Ductal Carcinoma NA None 4.4 17.23 17.73 Y N N CGPLBR37 Infiltration Ductal Carcinoma NA None 4.4 9.39 9.39 Y N N CGPLBR38 Infiltration Ductal Carcinoma Moderate None 4.0 5.77 5.77 Y Y Y CGPLBR40 Infiltration Ductal Carcinoma Poor None 4.6 15.69 15.69 Y Y Y CGPLBR41 Infiltration Ductal Carcinoma Moderate None 4.5 11.56 11.5

Y N Y CGPLBR45 Infiltration Ductal Carcinoma NA None 4.5 20.36 20.3

Y N N CGPLBR46 Infiltration Ductal Carcinoma NA None 3.5 20.17 20.17 Y N N CGPLBR47 Infiltration Ductal Carcinoma NA None 4.5 13.89 13.89 Y N N CGPLBR48 Infiltration Ductal Carcinoma Poor None 3.9 7.07 7.07 Y Y Y CGPLBR49 Infiltration Ductal Carcinoma Poor None 4.0 5.74 5.74 Y N Y CGPLBR50 Infiltration Ductal Carcinoma NA None 4.5 45.58 27.78 Y N N CGPLBR51 Infiltration Ductal Carcinoma NA None 4.0 8.83 8.83 Y N N CGPLBR52 Infiltration Ductal Carcinoma NA None 4.5 80.71 27.78 Y N N CGPLBR55 Infiltration Ductal Carcinoma Poor None 4.3 4.57 4.57 Y Y Y CGPLBR56 Infiltration Ductal Carcinoma NA None 4.5 22.16 22.16 Y N N CGPLBR57 Infiltration Ductal Carcinoma NA None 4.3 4.02 4.02 Y N Y CGPLBR59 Infiltration Ductal Carcinoma Moderate None 4.1 8.24 8.24 Y N Y CGPLBR60 Infiltration Ductal Carcinoma NA None 4.5 11.09 11.09 Y N N CGPLBR61 Infiltration Ductal Carcinoma Moderate None 4.1 13.25 13.25 Y N Y CGPLBR63 Infiltration Ductal Carcinoma Moderate None 4.0 6.19 6.19 Y Y Y CGPLBR65 Infiltration Ductal Carcinoma NA None 3.5 41.75 35.71 Y N N CGPLBR68 Infiltration Ductal Carcinoma Poor None 3.4 10.41 10.41 Y N Y CGPLBR69 Infiltration Ductal Carcinoma Moderate None 4.4 4.07 4.07 Y Y Y CGPLBR70 Infiltration Ductal Carcinoma Moderate None 3.4 11.94 11.94 Y Y Y CGPLBR71 Infiltration Ductal Carcinoma Poor None 3.1 7.64 7.64 Y Y Y CGPLBR72 Infiltration Ductal Carcinoma Well None 3.9 4.4

4.43 Y Y Y CGPLBR73 Infiltration Ductal Carcinoma Moderate None 3.3 14.

9 14.69 Y Y Y CGPLBR76 Infiltration Ductal Carcinoma Well None 4.9 8.71 8.71 Y Y Y CGPLBR81 Infiltration Ductal Carcinoma NA None 2.5 83.14 50.00 Y N N CGPLBR82 Infiltration Lobular Carcinoma Moderate None 4.8 23.39 23.39 Y N Y CGPLBR83 Infiltration Ductal Carcinoma Moderate None 3.7 100.17 100.17 Y Y Y CGPLBR84 Infiltration Ductal Carcinoma NA NA 3.6 16.95 16.95 Y N N CGPLBR87 Papillary Carcinoma Well None 3.6 277.39 69.44 Y Y Y CGPLBR88 Infiltration Ductal Carcinoma Poor None 3.6 49.75 49.75 Y Y Y CGPLBR90 Infiltration Ductal Carcinoma NA None 3.0 14.24 14.24 Y N N CGPLBR91 Infiltration Lobular Carcinoma Poor None 3.2 22.41 22.41 Y N Y CGPLBR92 Infiltration Medullary Carcinoma Poor None 3.1 81.00 81.00 Y Y Y CGPLBR93 Infiltration Ductal Carcinoma Moderate None 3.3 27.94 27.94 Y N Y CGPLH189 NA NA NA 5.0 5.84 5.84 Y N N CGPLH190 NA NA NA 4.7 18.07 18.07 Y N N CGPLH192 NA NA NA 4.7 12.19 12.19 Y N N CGPLH193 NA NA NA 5.0 5.47 5.47 Y N N CGPLH194 NA NA NA 5.0 9.98 9.98 Y N N CGPLH196 NA NA NA 5.0 11.69 11.69 Y N N CGPLH197 NA NA NA 5.0 5.69 5.69 Y N N CGPLH198 NA NA NA 5.0 4.36 4.36 Y N N CGPLH199 NA NA NA 5.0 9.77 9.77 Y N N CGPLH200 NA NA NA 5.0 5.

0 5.60 Y N N CGPLH201 NA NA NA 5.0 8.82 8.82 v N N CGPLH202 NA NA NA 5.0 5.54 5.54 Y N N CGPLH203 NA NA NA 5.0 9.03 9.03 Y N N CGPLH205 NA NA NA 5.0 4.74 4.74 Y N N CGPLH208 NA NA NA 5.0 4.67 4.67 Y N N CGPLH209 NA NA NA 5.0 5.15 5.15 Y N N CGPLH210 NA NA NA 5.0 5.41 5.41 Y N N CGPLH211 NA NA NA 5.0 6.24 6.24 Y N N CGPLH300 NA NA NA 4.4 6.75 6.75 Y N N CGPLH307 NA NA NA 4.5 3.50 3.50 Y N N CGPLH308 NA NA NA 4.5 6.01 6.01 Y N N CGPLH309 NA NA NA 4.5 5.21 5.21 Y N N CGPLH310 NA NA NA 4.5 15.25 15.25 Y N N CGPLH311 NA NA NA 4.5 4.47 4.47 Y N N CGPLH314 NA NA NA 4.5 9.62 9.62 Y N N CGPLH314 NA NA NA 4.4 16.24 16.24 Y N N CGPLH315 NA NA NA 4.2 11.55 11.55 Y N N CGPLH316 NA NA NA 4.5 28.92 27.7

Y N N CGPLH317 NA NA NA 4.5 7.62 7.62 Y N N CGPLH319 NA NA NA 4.2 4.41 4.41 Y N N CGPLH320 NA NA NA 4.5 6.93 6.93 Y N N CGPLH322 NA NA NA 4.2 8.17 8.17 Y N N CGPLH324 NA NA NA 5.0 6.

3 6.63 Y N N CGPLH325 NA NA NA 4.6 4.15 4.15 Y N N CGPLH326 NA NA NA 4.5 6.06 6.06 Y N N CGPLH327 NA NA NA 1.8 1.24 1.24 Y N N CGPLH328 NA NA NA 4.4 3.42 3.42 Y N N CGPLH328 NA NA NA 4.9 5.47 5.47 Y N N CGPLH329 NA NA NA 4.5 5.27 5.27 Y N N CGPLH330 NA NA NA 4.3 10.21 10.21 Y N N CGPLH331 NA NA NA 4.6 2.

3 2.63 Y N N CGPLH331 NA NA NA 4.3 4.15 4.15 Y N N CGPLH333 NA NA NA 4.7 4.06 4.06 Y N N CGPLH335 NA NA NA 4.4 9.39 9.39 Y N N CGPLH336 NA NA NA 4.6 6.

4 6.64 Y N N CGPLH337 NA NA NA 4.2 4.48 4.48 Y N N CGPLH338 NA NA NA 4.5 59.44 27.78 Y N N CGPLH339 NA NA NA 4.5 12.27 12.27 Y N N CGPLH340 NA NA NA 4.5 4.86 4.86 Y N N CGPLH341 NA NA NA 4.1 7.62 7.62 Y N N CGPLH342 NA NA NA 4.2 18.29 18.29 Y N N CGPLH343 NA NA NA 4.5 3.49 3.49 Y N N CGPLH344 NA NA NA 4.2 8.41 8.41 Y N N CGPLH345 NA NA NA 4.5 9.73 9.73 Y N N CGPLH346 NA NA NA 4.5 7.86 7.86 Y N N CGPLH35 NA NA NA 4.0 13.15 13.15 Y N Y CGPLH350 NA NA NA 3.5 6.09 6.09 Y N N CGPLH351 NA NA NA 4.0 15.91 15.91 Y N N CGPLH352 NA NA NA 4.2 6.47 6.47 Y N N CGPLH353 NA NA NA 4.2 4.47 4.47 Y N N CGPLH354 NA NA NA 4.2 17.49 17.49 Y N N CGPLH355 NA NA NA 4.2 11.58 11.58 Y N N CGPLH356 NA NA NA 4.5 3.94 3.94 Y N N CGPLH357 NA NA NA 4.2 11.79 11.79 Y N N CGPLH358 NA NA NA 4.2 21.0

21.0

Y N N CGPLH36 NA NA NA 4.0 13.00 13.0O Y N Y CGPLH360 NA NA NA 4.2 3.48 3.48 Y N N CGPLH361 NA NA NA 4.3 6.98 6.98 Y N N CGPLH362 NA NA NA 4.4 8.49 8.49 Y N N CGPLH363 NA NA NA 4.5 4.44 4.44 Y N N CGPLH364 NA NA NA 4.5 17.31 17.31 Y N N CGPLH365 NA NA NA 4.5 0.55 0.55 Y N N CGPLH366 NA NA NA 4.5 4.88 4.88 Y N N CGPLH367 NA NA NA 4.4 6.48 6.48 Y N N CGPLH368 NA NA NA 4.3 2.63 2.63 Y N N CGPLH369 NA NA NA 4.3 10.18 10.18 Y N N CGPLH369 NA NA NA 4.4 10.71 10.71 Y N N CGPLH37 NA NA NA 4.0 9.73 9.73 Y N Y CGPLH370 NA NA NA 4.5 7.22 7.22 Y N N CGPLH371 NA NA NA 4.6 5.62 5.62 Y N N CGPLH380 NA NA NA 4.2 6.61 6.61 Y N N CGPLH381 NA NA NA 4.2 27.38 27.33 Y N N CGPLH382 NA NA NA 4.5 11.58 11.58 Y N N CGPLH383 NA NA NA 4.5 25.50 25.50 Y N N CGPLH384 NA NA NA 4.5 15.

15.

Y N N CGPLH385 NA NA NA 4.5 19.35 19.35 Y N N CGPLH386 NA NA NA 4.5 6.46 6.46 Y N N CGPLH386 NA NA NA 4.6 6.54 6.54 Y N N CGPLH387 NA NA NA 4.5 6.19 6.19 Y N N CGPLH388 NA NA NA 4.5 6.

2 6.62 Y N N CGPLH389 NA NA NA 4.6 14.78 14.78 Y N N CGPLH390 NA NA NA 4.5 12.14 12.14 Y N N CGPLH391 NA NA NA 4.5 8.88 8.88 Y N N CGPLH391 NA NA NA 4.5 8.37 8.37 Y N N CGPLH392 NA NA NA 4.5 8.39 8.39 Y N N CGPLH393 NA NA NA 4.5 5.27 5.27 Y N N CGPLH394 NA NA NA 4.4 3.79 3.79 Y N N CGPLH395 NA NA NA 4.4 9.56 9.5

Y N N CGPLH395 NA NA NA 4.4 5.40 5.40 Y N N CGPLH396 NA NA NA 4.4 20.31 20.31 Y N N CGPLH398 NA NA NA 4.3 13.01 13.01 Y N N CGPLH399 NA NA NA 4.4 4.79 4.79 Y N N CGPLH400 NA NA NA 4.4 7.70 7.70 Y N N CGPLH400 NA NA NA 4.4 6.26 6.26 Y N N CGPLH401 NA NA NA 4.3 13.01 13.01 Y N N CGPLH401 NA NA NA 4.4 11.13 11.13 Y N N CGPLH402 NA NA NA 4.5 2.89 2.89 Y N N CGPLH403 NA NA NA 4.3 4.41 4.41 Y N N CGPLH404 NA NA NA 4.2 6.38 6.38 Y N N CGPLH405 NA NA NA 4.4 7.28 7.2

Y N N CGPLH406 NA NA NA 4.2 5.40 5.40 Y N N CGPLH407 NA NA NA 4.0 13.30 13.30 Y N N CGPLH408 NA NA NA 4.2 5.18 5.18 Y N N CGPLH409 NA NA NA 3.7 3.98 3.98 Y N N CGPLH410 NA NA NA 4.1 6.91 6.91 Y N N CGPLH411 NA NA NA 4.1 3.30 3.30 Y N N CGPLH412 NA NA NA 4.1 5.55 5.55 Y N N CGPLH413 NA NA NA 4.5 8.18 8.1

Y N N CGPLH414 NA NA NA 3.8 5.85 5.85 Y N N CGPLH415 NA NA NA 4.7 10.20 10.20 Y N N CGPLH416 NA NA NA 4.5 11.73 11.73 Y N N CGPLH417 NA NA NA 4.2 10.98 10.9

Y N N CGPLH418 NA NA NA 4.5 10.96 10.96 Y N N CGPLH419 NA NA NA 4.5 10.17 10.17 Y N N CGPLH42 NA NA NA 4.0 14.30 14.30 Y N Y CGPLH420 NA NA NA 4.2 12.32 12.32 Y N N CGPLH422 NA NA NA 4.6 5.42 5.42 Y N N CGPLH423 NA NA NA 4.2 2.85 2.85 Y N N CGPLH424 NA NA NA 4.7 1.66 1.66 Y N N CGPLH425 NA NA NA 4.4 5.98 5.98 Y N N CGPLH426 NA NA NA 4.4 2.

4 2.84 Y N N CGPLH427 NA NA NA 4.4 10.86 10.86 Y N N CGPLH428 NA NA NA 4.5 6.27 6.27 Y N N CGPLH429 NA NA NA 4.5 3.89 3.89 Y N N CGPLH43 NA NA NA 4.0 8.50 8.50 Y N Y CGPLH430 NA NA NA 4.2 10.33 10.33 Y N N CGPLH431 NA NA NA 4.8 12.81 12.81 Y N N CGPLH432 NA NA NA 4.8 2.42 2.42 Y N N CGPLH434 NA NA NA 4.6 8.83 8.83 Y N N CGPLH435 NA NA NA 4.5 8.95 8.95 Y N N CGPLH436 NA NA NA 4.5 4.29 4.29 Y N N CGPLH437 NA NA NA 4.6 18.07 18.07 Y N N CGPLH438 NA NA NA 4.8 16.62 16.62 Y N N CGPLH439 NA NA NA 4.7 4.38 4.38 Y N N CGPLH440 NA NA NA 4.7 4.32 4.32 Y N N CGPLH441 NA NA NA 4.7 7.80 7.80 Y N N CGPLH442 NA NA NA 4.5 6.15 6.15 Y N N CGPLH443 NA NA NA 4.4 3.44 3.44 Y N N CGPLH444 NA NA NA 4.4 4.12 4.12 Y N N CGPLH445 NA NA NA 4.4 4.38 4.38 Y N N CGPLH446 NA NA NA 4.4 2.

2 2.92 Y N N CGPLH447 NA NA NA 4.6 3.87 3.87 Y N N CGPLH448 NA NA NA 4.4 5.29 5.29 Y Y N CGPLH449 NA NA NA 4.5 3.77 3.77 Y N N CGPLH45 NA NA NA 4.0 10.85 10.85 Y N Y CGPLH450 NA NA NA 4.5 5.62 5.62 Y N N CGPLH451 NA NA NA 4.6 7.24 7.24 Y N N CGPLH452 NA NA NA 4.4 2.54 2.54 Y N N CGPLH453 NA NA NA 4.6 9.11 9.11 Y N N CGPLH455 NA NA NA 4.4 2.64 2.64 Y N N CGPLH455 NA NA NA 4.5 2.42 2.42 Y N N CGPLH456 NA NA NA 4.5 3.11 3.11 Y N N CGPLH457 NA NA NA 4.4 5.92 5.92 Y N N CGPLH458 NA NA NA 4.5 16.04 16.04 Y N N CGPLH459 NA NA NA 4.4 6.52 6.52 Y N N CGPLH46 NA NA NA 4.0 8.25 8.25 Y N Y CGPLH460 NA NA NA 4.6 5.24 5.24 Y N N CGPLH463 NA NA NA 4.5 22.77 22.77 Y N N CGPLH464 NA NA NA 4.4 2.90 2.90 Y N N CGPLH465 NA NA NA 4.5 4.76 4.76 Y N N CGPLH466 NA NA NA 4.6 5.68 5.68 Y N N CGPLH466 NA NA NA 4.5 6.75 6.75 Y N N CGPLH467 NA NA NA 4.5 4.

9 4.59 Y N N CGPLH468 NA NA NA 4.5 11.19 11.19 Y N N CGPLH469 NA NA NA 4.5 3.25 3.25 Y N N CGPLH47 NA NA NA 4.0 7.43 7.43 Y N Y CGPLH470 NA NA NA 4.5 13.

4 13.64 Y N N CGPLH471 NA NA NA 4.3 13.00 13.00 Y N N CGPLH472 NA NA NA 4.2 10.17 10.17 Y N N CGPLH473 NA NA NA 4.3 2.98 2.98 Y N N CGPLH474 NA NA NA 4.3 29.15 29.15 Y N N CGPLH475 NA NA NA 4.0 7.26 7.26 Y N N CGPLH476 NA NA NA 4.3 6.16 6.16 Y N N CGPLH477 NA NA NA 4.3 15.21 15.21 Y N N CGPLH478 NA NA NA 4.4 7.29 7.29 Y N N CGPLH479 NA NA NA 4.5 8.73 8.73 Y N N CGPLH48 NA NA NA 4.0 6.38 6.38 Y N Y CGPLH480 NA NA NA 4.4 10.62 10.62 Y N N CGPLH481 NA NA NA 4.3

.75

.75 Y N N CGPLH482 NA NA NA 4.3 23.58 23.58 Y N N CGPLH483 NA NA NA 4.4 14.44 14.44 Y N N CGPLH484 NA NA NA 4.2 14.32 14.32 Y N N CGPLH485 NA NA NA 4.3 9.64 9.64 Y N N CGPLH486 NA NA NA 4.3 10.16 10.16 Y N N CGPLH487 NA NA NA 4.4 6.11 6.11 Y N N CGPLH488 NA NA NA 4.5 7.88 7.88 Y N N CGPLH49 NA NA NA 4.0 6.60 6.60 Y N Y CGPLH490 NA NA NA 4.5 4.18 4.18 Y N N CGPLH491 NA NA NA 4.5 13.16 13.16 Y N N CGPLH492 NA NA NA 4.5 3.8

3.8

Y N N CGPLH493 NA NA NA 4.5 25.06 25.06 Y N N CGPLH494 NA NA NA 4.4 5.24 5.24 Y N N CGPLH495 NA NA NA 4.4 5.03 5.03 Y N N CGPLH496 NA NA NA 4.5 34.01 27.78 Y N N CGPLH497 NA NA NA 4.5 8.24 8.24 Y N N CGPLH497 NA NA NA 4.4 5.88 5.88 Y N N CGPLH498 NA NA NA 4.4 5.33 5.33 Y N N CGPLH499 NA NA NA 4.5 7.

5 7.85 Y N N CGPLH50 NA NA NA 4.0 7.05 7.05 Y N Y CGPLH500 NA NA NA 4.5 3.49 3.49 Y N N CGPLH501 NA NA NA 4.3 6.29 6.29 Y N N CGPLH502 NA NA NA 4.5 2.74 2.24 Y N N CGPLH503 NA NA NA 4.5 11.01 11.01 Y N N CGPLH504 NA NA NA 4.3 6.60 6.60 Y N N CGPLH504 NA NA NA 4.2 10.02 10.02 Y N N CGPLH505 NA NA NA 4.1 5.23 5.23 Y N N CGPLH506 NA NA NA 4.5 12.23 12.23 Y N N CGPLH507 NA NA NA 4.1 9.89 9.89 Y N N CGPLH508 NA NA NA 4.5 8.88 8.88 Y N N CGPLH508 NA NA NA 4.4 9.55 9.55 Y N N CGPLH509 NA NA NA 4.0 9.79 9.79 Y N N CGPLH51 NA NA NA 4.0 7.85 7.85 Y N Y CGPLH510 NA NA NA 4.2 14.20 14.20 Y N N CGPLH511 NA NA NA 4.5 12.94 12.94 Y N N CGPLH512 NA NA NA 4.3 8.60 8.60 Y N N CGPLH513 NA NA NA 4.3 6.54 6.54 Y N N CGPLH514 NA NA NA 4.4 10.94 10.94 Y N N CGPLH515 NA NA NA 4.5 8.71 8.71 Y N N CGPLH516 NA NA NA 4.5 7.32 7.32 Y N N CGPLH517 NA NA NA 4.6 5.16 5.16 Y N N CGPLH517 NA NA NA 4.5 9.74 9.74 Y N N CGPLH518 NA NA NA 4.4 5.92 5.92 Y N N CGPLH519 NA NA NA 4.4 6.96 6.96 Y N N CGPLH522 NA NA NA 4.0 9.90 9.90 Y N Y CGPLH520 NA NA NA 4.3 8.27 8.27 Y N N CGPLH54 NA NA NA 4.0 14.18 14.18 Y N Y CGPLH55 NA NA NA 4.0 7.35 7.35 Y N Y CGPLH56 NA NA NA 4.0 5.20 5.20 Y N Y CGPLH57 NA NA NA 4.0 7.15 7.15 Y N Y CGPLH59 NA NA NA 4.0 6.03 6.03 Y N Y CGPLH625 NA NA NA 4.5 2.64 2.64 Y N N CGPLH625 NA NA NA 4.5 2.69 2.69 Y N N CGPLH626 NA NA NA 4.0 11.12 11.12 Y N N CGPLH63 NA NA NA 4.0 10.10 10.10 Y N Y CGPLH639 NA NA NA 4.5 2.00 2.00 Y N N CGPLH64 NA NA NA 4.0 8.03 8.03 Y N Y CGPLH640 NA NA NA 4.5 9.36 9.36 Y N N CGPLH642 NA NA NA 4.5 4.99 4.99 Y N N CGPLH643 NA NA NA 4.4 7.12 7.12 Y N N CGPLH644 NA NA NA 4.4 5.06 5.06 Y N N CGPLH646 NA NA NA 4.4 6.75 6.75 Y N N CGPLH75 NA NA NA 4.0 3.87 3.87 Y N Y CGPLH76 NA NA NA 4.0 4.03 4.03 Y N Y CGPLH77 NA NA NA 4.0 5.89 5.89 Y N Y CGPLH78 NA NA NA 4.0 2.51 2.51 Y N Y CGPLH79 NA NA NA 4.0 3.68 3.68 Y N Y CGPLH80 NA NA NA 4.0 1.94 1.94 Y N Y CGPLH81 NA NA NA 4.0 5.16 5.16 Y N Y CGPLH82 NA NA NA 4.0 3.30 3.30 Y N Y CGPLH83 NA NA NA 4.0 5.04 5.04 Y N Y CGPLH84 NA NA NA 4.0 3.33 3.33 Y N Y CGPLLU13 Adenocarcinoma NA Bone 5.0 7.

7 7.67 Y N Y CGPLLU13 Adenocarcinoma NA Bone 4.5 8.39 8.39 Y N Y CGPLLU13 Adenocarcinoma NA Bone 3.2 8.66 8.66 Y N Y CGPLLU13 Adenocarcinoma NA Bone 5.0 5.97 5.97 Y N Y CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y CGPLLU14 Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y CGPLLU144 Adenocarcinoma Poor None 3.5 31.51 31.51 Y Y Y CGPLLU147 Adenosquamous Carc

noma Poor None 3.8 6.72 6.72 Y Y Y CGPLLU161 Adenocarcinoma Well None 4.0 83.04 83.04 Y N Y CGPLLU162 Adenocarcinoma Moderate None 3.1 40.32 40.32 Y Y Y CGPLLU163 Adenocarcinoma Poor None 5.0 54.03 54.03 Y Y Y CGPLLU165 Adenocarcinoma Well None 4.5 20.13 20.13 Y Y Y CGPLLU168 Adenocarcinoma Poor None 4.3 19.38 19.38 Y Y Y CGPLLU169 Squamous Cel Carcinoma Moderate None 4.2 13.70 13.70 Y N Y CGPLLU175 Squamous Cel Carcinoma Moderate None 4.4 16.84 16.84 Y Y Y CGPLLU176 Adenosquamous Carc

noma Moderate None 3.2 7.86 7.86 Y Y Y CGPLLU177 Adenocarcinoma NA None 3.9 19.07 19.07 Y Y Y CGPLLU180 Large Cel Carcinoma Poor None 3.2 19.31 19.31 Y Y Y CGPLLU198 Adenocarcinoma Moderate None 4.2 14.09 14.09 Y Y Y CGPLLU202 Adenocarcinoma NA None 4.4 24.72 24.72 Y Y Y CGPLLU203 Squamous Cel Carcinoma Well None 4.2 26.24 26.24 Y N Y CGPLLU205 Adenocarcinoma Poor None 4.0 18.56 18.55 Y Y Y CGPLLU206 Squamous Cel Carcinoma Poor None 3.5 18.24 18.24 Y Y Y CGPLLU207 Adenocarcinoma Well None 4.0 17.29 17.29 Y Y Y CGPLLU208 Adenocarcinoma Moderate None 3.0 24.34 24.34 Y Y Y CGPLLU209 Large Cel Carcinoma Poor None 5.5 53.95 53.95 Y Y Y CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.48 Y N Y Poor Brain, Pleura CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y Poor Brain, Pleura CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y Poor Brain, Pleura CGPLLU244 Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y Poor Brain, Pleura CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y CGPLLU245 Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y CGPLLU246 Adenocarcinoma Poor P

eura 5.5 18.51 18.51 Y N Y CGPLLU246 Adenocarcinoma Poor P

eura 5.5 18.51 18.51 Y N Y CGPLLU246 Adenocarcinoma Poor P

eura 5.5 18.51 18.51 Y N Y CGPLLU246 Adenocarcinoma Poor P

eura 5.5 18.51 18.51 Y N Y CGPLLU264 Adenocarcinoma NA Lung 4.0 22.97 22.97 Y N Y CGPLLU264 Adenocarcinoma NA Lung 4.5 10.53 10.53 Y N Y CGPLLU264 Adenocarcinoma NA Lung 3.0 7.15 7.15 Y N Y CGPLLU264 Adenocarcinoma NA Lung 4.0 9.

0 9.60 Y N Y CGPLLU265 Adenocarcinoma NA Lung 4.2 7.16 7.16 Y N Y CGPLLU265 Adenocarcinoma NA None 4.0 8.11 8.11 Y N Y CGPLLU265 Adenocarcinoma NA None 4.2 7.53 7.53 Y N Y CGPLLU265 Adenocarcinoma NA None 5.0 16.17 16.17 Y N Y CGPLLU266 Adenocarcinoma Moderate None 5.0 5.32 5.32 Y N Y CGPLLU266 Adenocarcinoma Moderate None 3.5 6.31 6.31 Y N Y CGPLLU266 Adenocarcinoma Moderate None 5.0 7.64 7.64 Y N Y CGPLLU266 Adenocarcinoma Moderate None 5.0 14.39 14.39 Y N Y CGPLLU267 Squamous Cel Carcinoma Poor Lung 4.5 2.87 2.87 Y N Y CGPLLU267 Squamous Cel Carcinoma Poor Lung 4.5 3.34 3.34 Y N Y CGPLLU267 Squamous Cel Carcinoma Poor Lung 3.5 3.00 3.00 Y N Y CGPLLU269 Adenocarcinoma NA Brain, Liver, 5.0 11.40 11.40 Y N Y Bone, P

eura CGPLLU269 Adenocarcinoma NA Brain, Liver, 5.0 8.

5 8.35 Y N Y Bone, P

eura CGPLLU269 Adenocarcinoma NA Brain, Liver, 3.5 17.79 17.79 Y N Y Bone, P

eura CGPLLU271 Adenocarcinoma NA P

eura 4.0 4.70 4.70 Y N Y CGPLLU271 Adenocarcinoma NA P

eura 5.0 18.

18.

Y N Y CGPLLU271 Adenocarcinoma NA P

eura 4.5 13.84 13.84 Y N Y CGPLLU271 Adenocarcinoma NA P

eura 3.5 13.46 13.46 Y N Y CGPLLU271 Adenocarcinoma NA P

eura 4.0 13.77 13.77 Y N Y CGPLLU43 Adenocarcinoma Moderate None 4.9 2.17 2.17 Y N Y CGPLLU43 Adenocarcinoma Moderate None 3.7 3.26 3.26 Y N Y CGPLLU43 Adenocarcinoma Moderate None 4.0 4.12 4.12 Y N Y CGPLLU43 Adenocarcinoma Moderate None 3.7 8.20 8.20 Y N Y CGPLLU86 Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y CGPLLU86 Adenocarcinoma NA Lung 4.C 7.90 7.90 Y N Y CGPLLU86 Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y CGPLLU86 Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y CGPLLU88 Adenocarcinoma NA None 5.0 27.

27.

Y N Y CGPLLU88 Adenocarcinoma NA None 5.0 6.49 6.49 Y N Y CGPLLU88 Adenocarcinoma NA None 4.0 3.04 3.04 Y N Y CGPLLU89 Adenocarcinoma NA Brain, Bone, Lung 8.0 8.43 8.43 Y N Y CGPLLU89 Adenocarcinoma NA Brain, Bone, Lung 8.0 8.43 8.43 Y N Y CGPLLU89 Adenocarcinoma NA Brain, Bone, Lung 8.0 8.43 8.43 Y N Y CGPLOV11 Endometrioid Adenocarcinoma Moderate Omentum 3.4 17.35 17.35 Y Y Y CGPLOV12 Endometrioid Adenocarcinoma NA None 3.2 12.44 12.44 Y N Y CGPLOV13 Endometrioid Adenocarcinoma Poor Omentum 3.8 27.00 27.00 Y Y Y CGPLOV15 Adenocarcinoma Poor None 5.0 4.77 4.77 Y Y Y CGPLOV16 Serous Adenocarcinoma Moderate None 4.5 27.28 27.28 Y Y CGPLOV19 Endometrioid Adenocarcinoma Moderate None 5.0 23.46 23.46 Y Y Y CGPLOV20 Endometrioid Adenocarcinoma Poor None 4.2 5.67 5.67 Y Y Y CGPLOV21 Serous Adenocarcinoma Poor Omentum, 4.3 56.32 56.32 Y Y Y Appendix CGPLOV22 Serous Adenocarcinoma Well None 4.6 17.42 17.42 Y Y Y CGPLOV23 Serous Adenocarcinoma Poor None 5.0 26.73 26.73 Y N Y CGPLOV24 Germ Cell Tumor Poor None 4.2 10.71 10.71 Y N Y CGPLOV25 Germ Cell Tumor Poor None 4.8 6.78 6.78 Y N Y CGPLOV26 Germ Cell Tumor Poor None 4.5 27.90 27.90 Y N Y CGPLOV28 Serous Carcinoma NA None 3.2 10.74 10.74 Y N Y CGPLOV31 Clea

 Cell adenocarcinoma NA None 4.0 14.45 14.45 Y N Y CGPLOV32 Mucinous Cystadenoma NA None 3.2 27.36 27.36 Y N Y CGPLOV37 Serous Carcinoma NA None 3.2 46.88 46.88 Y N Y CGPLOV38 Serous Carcinoma NA None 2.4 34.29 34.29 Y N Y CGPLOV40 Serous Carcinoma NA Omentum, Uterus, 1.

193.60 156.25 Y N Y Appendix CGPLOV41 Serous Carcinoma NA Omentum, Uterus, 4.4 10.03 10.03 Y N Y Cervix CGPLOV42 Serous Carcinoma NA None 4.2 49.51 49.51 Y N Y CGPLOV43 Mucinous Cystadenocarcinoma NA None 4.4 9.09 9.09 Y N Y CGPLOV44 Mucinous Adenocarcinoma NA None 4.5 8.79 8.79 Y N Y CGPLOV46 Serous Carcinoma NA None 4.1 8.97 8.97 Y N Y CGPLOV47 Serous Cystadenoma NA None 4.5 19.35 19.35 Y N Y CGPLOV48 Serous Carcinoma NA None 3.5 22.80 22.80 Y N Y CGPLOV49 Serous Carcinoma NA None 4.2 16.48 16.48 Y N Y CGPLOV50 Serous Carcinoma NA None 4.5 8.89 8.89 Y N Y CGPLPA112 NA NA None 35 18.52 18.52 Y N N CGPLPA113 NA NA None 4.8 8.24 8.24 Y N N CGPLPA114 NA NA None 4.8 26.43 26.43 Y N N CGPLPA115 NA NA NA 5.0 31.41 31.41 Y N N CGPLPA117 NA NA None 3.4 2.29 2.29 Y N N CGPLPA118 Intra-Ampullary Bile Duct NA None 3.8 9.93 9.93 Y N Y CGPLPA122 Intra-Pancreatic Bi

e Duct NA None 3.8 66.54 32.89 Y N Y CGPLPA124 Intra-Ampullary Bile Duct moderate None 4.6 29.24 27.17 Y N Y CGPLPA125 Intra-Pancreatic Bi

e Duct poor None 2.7 8.31 8.31 Y N N CGPLPA126 Intra-Pancreatic Bi

e Duct NA None 4.3 80.56 29.07 Y N Y CGPLPA127 Extra-Pancreatic Bile Duct NA NA 3.0 20.60 20.60 Y N N CGPLPA128 Intra-Pancreatic Bi

e Duct NA None 3.9 5.91 5.91 Y N Y CGPLPA129 Intra-Pancreatic Bi

e Duct NA None 4.6 27.07 27.07 Y N Y CGPLPA130 Intra-Ampullary Bile Duct well None 4.0 4.34 4.34 Y N Y CGPLPA131 Intra-Pancreatic Bi

e Duct NA None 3.9 68.95 32.05 Y N Y CGPLPA134 Intra-Pancreatic Bi

e Duct NA None 4.1 58.08 30.49 Y N Y CGPLPA135 Intra-Pancreatic Bi

e Duct NA NA 3.9 4.22 4.22 Y N N CGPLPA136 Intra-Pancreatic Bi

e Duct NA None 4.1 20.23 20.23 Y N Y CGPLPA137 NA NA NA 4.0 5.75 5.75 Y N N CGPLPA139 NA NA NA 4.0 14.89 14.89 Y N N CGPLPA14 Ductal Adenocarc

noma Poor None 4.0 1.30 1.30 Y N N CGPLPA140 Intra Pancreatic Bi

e Duct Poor None 4.7 29.34 26.60 Y N Y CGPLPA141 Intra Pancreatic Bi

e Duct Moderate None 2.8 53.

7 44.64 Y N N CGPLPA15 Ductal Acenocarc

noma Well Lymph Node 4.0 1.92 1.92 Y N N CGPLPA155 NA NA NA 4.0 25.72 25.72 Y N N CGPLPA156 Ductal Adenocarc

noma Poor Lymph Node 4.5 7.54 7.54 Y N N CGPLPA165 Intra-Pancreatic Bile Duck well None 3.9 10.48 10.48 Y N N with Medullary Features CGPLPA168 Extra-Pancreatic Bile Duct NA NA 3.0 139.12

4.72 Y N N CGPLPA17 Ductal Adenocarc

noma Well Lymph Node 4.0 13.08 13.08 Y N N CGPLPA184 Intra-Pancreatic Bi

e Duct NA None NA NA NA Y N N CGPLPA187 Intra-Pancreatic Bi

e Duct NA None NA NA NA Y N N CGPLPA23 Ductal Adenocarc

noma Moderate Lymph Node 4.0 16.62 16.62 Y N N CGPLPA25 Ductal Adenocarc

noma Poor Lymph Node 4.0 8.71 8.71 Y N N CGPLPA26 Ductal Adenocarc

noma Well Lymph Node 4.0 6.97 6.97 Y N N CGPLPA28 Ductal Adenocarc

noma Well Lymph Node 4.0 18.13 18.13 Y N N CGPLPA33 Ductal Adenocarc

noma Well Lymph Node 4.0 1.

0 1.80 Y N N CGPLPA34 Ductal Adenocarc

noma Well Lymph Node 4.0 3.36 3.3

Y N N CGPLPA37 Ductal Adenocarc

noma NA Lymph Node 4.0 21.83 21.83 Y N N CGPLPA38 Ductal Adenocarc

noma Moderate None 4.0 5.29 5.29 Y N N CGPLPA39 Ductal Adenocarc

noma Well Lymph Node 4.0 11.73 11.73 Y N N CGPLPA40 Ductal Adenocarc

noma Well Lymph Node 4.0 4.78 4.78 Y N N CGPLPA42 Ductal Adenocarc

noma Moderate Lymph Node 4.0 3.41 3.41 Y N N CGPLPA46 Ductal Adenocarc

noma Poor Lymph Node 4.0 0.74 0.74 Y N N CGPLPA47 Ductal Adenocarc

noma Well Lymph Node 4.0 6.01 6.01 Y N N CGPLPA48 Ductal Adenocarc

noma Well None NA NA NA Y N N CGPLPA52 Ductal Adenocarc

noma Moderate None 2.5 9.86 9.86 Y N N CGPLPA53 Ductal Adenocarc

noma Poor Lymph Node 3.0 14.48 14.4

Y N N CGPLPA58 Ductal Adenocarc

noma NA None 3.0 6.87 6.87 Y N N CGPLPA59 Ductal Adenocarcinoma or Well Lymph Node NA NA NA Y N N Adenome CGPLPA67 Ductal Adenocarc

noma Well Lymph Node 3.2 9.72 9.72 Y N N CGPLPA69 Ductal Adenocarc

noma Well None 2.0 1.72 1.72 Y N N CGPLPA71 Ductal Adenocarc

noma Well Lymph Node 2.2 39.07 39.07 Y N N CGPLPA74 Ductal Adenocarc

noma Moderate Lymph Node 2.5 4.99 4.99 Y N N CGPLPA76 Ductal Adenocarc

noma Poor None 2.5 23.19 23.19 Y N N CGPLPA85 Ductal Adenocarc

noma Poor Lymph Node 3.0 152.46 41.67 Y N N CGPLPA86 Ductal Adenocarc

noma Moderate Lymph Node 2.5 11.02 11.02 Y N N CGPLPA92 Ductal Adenocarc

noma NA Lymph Node 2.0 5.34 5.34 Y N N CGPLPA93 Ductal Adenocarc

noma Poor None 3.0 96.28 41.67 Y N N CGPLPA94 Ductal Adenocarc

noma NA Lymph Node 3.0 29.

6 29.65 Y N N CGPLPA95 Ductal Adenocarc

noma Well Lymph Node NA NA NA Y N N CGST102 Tubular Adenocarcinoma Moderate None 4.1 8.03 8.03 Y N Y CGST1

Mixed Carcinoma Moderate None 3.8 3.57 3.57 Y N N CGST110 Tubular Adenocarcinoma Moderate None 3.8 5.00 5.00 Y N Y CGST114 Tubular Adenocarcinoma Poor None 4.4 10.35 10.35 Y N Y CGST13 Signet Ring Cell Carcinoma Poor None 4.4 24.33 24.33 Y N Y CGST131 Signet ring cel carcinoma Poor None 4.0 4.28 4.28 Y N N CGST141 Signet Cell Carcinoma Poor None 4.4 10.84 10.84 Y N Y CGST16 Tubular Adenocarcinoma Poor None 4.0 40.69 40.69 Y N Y CGST18 Mucinous Adenocarcinoma Well None 4.3 9.78 9.78 Y N Y CGST2

Papillary Adenocarcinoma Moderate None 4.0 0.83 0.83 Y N N CGST26 Signet ring cel carcinoma Poor None 3.5 5.56 5.56 Y N N CGST28 Undifferentiated Ca

cinoma Poor None 4.0 5.86 5.86 Y N Y CGST30 Signet Ring Cell Carcinoma Poor None 3.0 4.22 4.22 Y N Y CGST32 Tubular Adenocarcinoma Moderate None 4.0 11.49 11.49 Y N Y CGST33 Tubular Adenocarcinoma Moderate None 3.5 5.71 5.71 Y N Y CGST38 Mucinous adenocarcinoma NA None 4.0 NA NA Y N N CGST39 Signet Ring Cell Carcinoma Poor None 3.5 20.69 20.69 Y N Y CGST4

Signet Ring Cell Carcinoma Poor None 3.5 7.83 7.83 Y N Y CGST45 Signet Ring Cell Carcinoma Poor None 3.8 7.14 7.14 Y N Y CGST47 Tubular Adenocarcinoma Moderate None 4.0 4.55 4.55 Y N Y CGST48 Tubular Adenocarcinoma Poor None 4.5 8.79 8.79 Y N Y CGST53 NA NA None 3.8 15.82 15.82 V N N CGST58 Signet Ring Cell Carcinoma Poor None 3.8 19.81 19.81 Y N Y CGST67 Tubular adenocarcinoma Moderate None 3.0 23.01 23.01 Y N N CGST77 Tubular adenocarcinoma Moderate None 4.5 15.09 15.09 Y N N CGST80 Mucinous Adenocarcinoma Poor None 4.5 8.56 8.56 Y N Y CGST8

Signet Ring Cell Carcinoma Poor None 3.5 37.32 37.32 Y N Y CGH14 NA NA NA NA NA NA Y N N CGH15 NA NA NA NA NA NA Y N N

NA denotes data not available or not app

icable for healthy individuals.

indicates data missing or illegible when filed

TABLE 2 APPENDIX B: Summary of targeted cfDNA analyses Bases Mapped Percent Mapped Fragment Profile Mutation Bases in Bases Mapped to Target to Target Total Distinct Patient Patient Type Timepoint Analysis Analysis Read Length Target Region to Genome Regions Regions Coverage Coverage CGCRC291 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7501485600 3771359756 50% 44345 10359 CGCRC292 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6736035200 3098886973 46% 36448 8603 CGCRC233 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6300244000 2818734206 45% 33117 5953 CGCRC294 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7786872600 3911796709 50% 46016 12071 CGCRC295 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 8240660200 3478059753 42% 40787 5826 CGCRC296 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 5718556500 2898549356 51% 33912 10180 CGCRC297 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7550826100 3717222432 49% 43545 5870 CGCRC298 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 12501036400 6096393764 49% 71196 9617 CGCRC299 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7812602900 4121569690 53% 48098 10338 CGCRC300 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8648090300 3962285136 46% 46364 5756 CGCRC301 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7538758100 3695480348 49% 43024 6618 CGCRC302 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8573658300 4349420574 51% 51006 13799 CGCRC303 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 5224046400 2505714343 48% 29365 8372 CGCRC304 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 5762112600 2942170530 51% 34462 10208 CGCRC305 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7213384100 3726953480 52% 43516 8589 CGCRC306 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7075579700 3552441899 50% 41507 7372 CGCRC307 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7572687100 3492191519 46% 40793 9680 CGCRC308 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7945738000 3895908986 49% 45224 11809 CGCRC309 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8487455800 3921079811 46% 45736 10739 CGCRC310 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 9003580500 4678812441 52% 54713 11139 CGCRC311 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6528162700 3276653864 50% 38324 6044 CGCRC312 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7683294300 3316719187 43% 38652 4622 CGCRC313 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 5874099200 2896148722 49% 33821 6506 CGCRC314 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 6883148500 3382767492 49% 39414 6664 CGCRC315 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7497252500 3775556051 50% 44034 8666 CGCRC315 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 10684720400 5533857153 52% 64693 14289 CGCRC317 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7086877600 3669434216 52% 43538 10944 CGCRC318 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 6880041100 3326357413 48% 39077 11571 CGCRC319 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7485342900 3982677483 53% 47327 10502 CGCRC320 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7056703200 3450648135 49% 40888 10198 CGCRC321 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 7203625900 3633396892 50% 43065 6499 CGCRC332 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7202969100 3758323705 52% 44580 3243 CGCRC333 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8767144700 4199126827 48% 49781 8336 CGCRC334 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7771869100 3944578280 51% 46518 5014 CGCRC335 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7972524600 4064901201 51% 48308 6151 CGCRC336 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8597346400 4333410573 50% 51390 7551 CGCRC337 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7399611700 3800666199 51% 45083 8092 CGGRC338 Colorectal Cancer Preoperative, Treatment naïve Y Y 100 80930 8029493700 4179383804 52% 49380 5831 CGCRC339 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7938963600 4095555110 52% 48397 3808 CGCRC340 Colorectal Cancer Preoperative, Treatment naïve Y N 100 80930 7214889500 3706643098 51% 43805 3014 CGCRC341 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8803159200 3668208527 42% 43106 11957 CGCRC342 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8478811500 342554088

40% 40328 9592 CGCRC344 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6942167800 3098232737 45% 36823 2300 CGCRC345 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8182868200 2383173431 29% 28233 7973 CGGRC346 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7448272300 3925056341 53% 46679 5582 CGCRC347 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 5804744500 2986809912 51% 35490 4141 CGCRC349 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6943451600 3533145275 51% 41908 5762 CGCRC350 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7434818400 3848923016 52% 45678 4652 CGCRC351 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7306546400 363691040

50% 43162 5205 CGCRC352 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7864655000 3336939252 42% 39587 4502 CGCRC353 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7501674800 3642919375 49% 43379 4666 CGCRC354 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7938270200 2379068977 30% 28256 4858 CGCRC356 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6013175900 3046754994 51% 36127 3425 CGGRC357 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6013464600 3022035300 50% 35813 4259 CGCRC358 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7227212400 3188723303 44% 37992 5286 CGCRC359 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7818567700 425110101  5% 5040 2566 CGCRC367 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6582043200 3363063597 51% 39844 5839 CGCRC368 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 8042242400 4101646000 51% 48636 11471 CGCRC370 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6940330100 3198954121 46% 38153 4826 CGCRC373 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6587201700 3120088035 47% 37234 5190 CGCRC376 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6727983100 3162416807 47% 37735 3445 CGCRC377 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6716339200 3131415570 47% 37160 4524 CGCRC378 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6523969900 2411096720 37% 28728 3239 CGCRC379 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6996252100 3371081103 48% 39999 2891 CGCRC380 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7097496300 2710244446 38% 32020 3261 CGCRC381 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6951936100 3287050681 47% 38749 9357 CGCRC382 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6959048700 2552325859 37% 30040 5148 CGCRC384 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7012798900 3293884583 47% 39158 3653 CGCRC385 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7542017900 3356570505 45% 39884 3686 CGCRC386 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6876059600 3064412286 45% 36431 2787 CGCRC387 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7399564700 3047254560 41% 36141 6675 CGCRC388 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6532692900 3137284885 48% 37285 5114 CGCRC389 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6651206300 3102100941 47% 36764 6123 CGCRC390 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 7260616800 3376667585 47% 40048 4368 CGCRC331 Colorectal Cancer Preoperative, Treatment naïve N Y 100 80930 6883624500 3202877881 47% 37978 5029 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930 7864415100 1991331171 25% 23601 3565 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930 7502591600 3730963390 50% 44262 3966 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930 6582515900 3187059470 48% 37813 3539 CGLU316 Lung Cancer Pre-treatment, Day −53 Y N 100 80930 6587281800 1947630979 30% 23094 4439 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930 6151628500 2748983603 45% 32462 8063 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930 7842910900 1147703178 15% 13565 4303 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930 5838083100 2291108925 39% 27067 4287 CGLU344 Lung Cancer Pre-treatment, Day −21 Y N 100 80930 7685989200 3722274529 48% 43945 3471 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7080245300 1271457982 18% 15109 2364 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7078131900 1482448715 21% 17583 4275 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 6904701700 2124660124 31% 25230 5278 CGLU369 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7003462200 3162195578 45% 37509 6062 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 6346267200 3053520676 48% 36137 6251 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 6517189900 3192984468 49% 38066 8040 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7767146300 3572598842 46% 42378 5306 CGLU373 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 7190999100 3273648804 46% 38784 4454 CGPLBR100 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7299964400 3750278051 51% 44794 3249 CGPLBR101 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7420822800 3810365416 51% 45565 9784 CGPLBR102 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 6679304900 3269688319 49% 38679 7613 CGPLBR103 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7040304400 3495542468 50% 41786 6748 CGPLBR104 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7188389200 3716096781 52% 44316 9448 CGPLBR38 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7810293900 4057576306 52% 48098 9868 CGPLBR39 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7745701500 3805623239 49% 45084 11065 CGPLBR40 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7558990500 3652442341 48% 43333 12948 CGPLBR41 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7900994600 3836600101 49% 45535 10847 CGPLBR44 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 7017744200 3269110569 47% 38672 8344 CGPLBR48 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 5629044200 2611554623 46% 30860 8652 CGPLBR49 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 5784711600 2673457893 46% 31274 10429 CGPLBR55 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8309154900 4306956261 52% 51143 8328 CGPLBR57 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8636181000 4391502618 51% 52108 5857 CGPLBR59 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8799457700 4152328555 47% 49281 5855 CGPLBR61 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8163706700 3952010628 48% 46755 8522 CGPLBR63 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7020533100 3542447304 50% 41956 4773 CGPLBR67 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 8254353900 3686093696 45% 43516 7752 CGPLBR68 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7629312300 4078969547 53% 48389 7402 CGPLBR69 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7571501500 3857354512 51% 45322 7047 CGPLBR70 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7251760700 3641333708 50% 43203 8884 CGPLBR71 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8515402600 4496696391 53% 53340 6805 CGPLBR72 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8556946900 4389761697 51% 52081 5632 CGPLBR73 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7959392300 4006933338 50% 47555 8791 CGPLBR74 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 8524536400 4063900599 48% 48252 7013 CGPLBR75 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 8250379100 3960599885 48% 46955 6319 CGPLBP76 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7774235200 3893522420 50% 46192 9628 CGPLBR77 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 7572797600 3255963429 43% 38568 8263 CGPLBR80 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 6845325800 3147476693 46% 37201 5595 CGPLBR82 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 8236705200 4170465005 51% 49361 12319 CGPLBR83 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7434568100 3676855019 49% 43628 5458 CGPLBR86 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7616282500 3644791327 48% 43490 7048 CGPLBR87 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 6194021300 3004882010 49% 35765 5306 CGPLBR88 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 6071567200 2847926237 47% 33945 10319 CGPLBR91 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7192457700 3480203404 48% 41570 9912 CGPLBP92 Breast Cancer Preoperative, Treatment naïve Y Y 100 80930 7678981800 3600279233 47% 42975 13580 CGPLBR93 Breast Cancer Preoperative, Treatment naïve N Y 100 80930 7605717800 3998713397 53% 47866 10329 CGPLBR96 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 6297446700 2463064737 39% 29341 7937 CGPLBR97 Breast Cancer Preoperative, Treatment naïve Y N 100 80930 7114921600 3557069027 50% 42488 10712 CGPLH35 Healthy Preoperative, Treatment naïve N Y 100 80930 6919126300 2312758764 33% 25570 1989 CGPLH36 Healthy Preoperative, Treatment naïve N Y 100 80930 6089923400 2038548115 33% 22719 1478 CGPLH37 Healthy Preoperative, Treatment naïve N Y 100 80930 5557270200 1935301929 35% 21673 2312 CGPLH42 Healthy Preoperative, Treatment naïve N Y 100 80930 5792045400 2388036949 41% 27197 2523 CGPLH43 Healthy Preoperative, Treatment naïve N Y 100 80930 5568321700 2017813329 36% 23228 1650 CGPLH45 Healthy Preoperative, Treatment naïve N Y 100 80930 8485593200 2770176078 33% 32829 3114 CGPLH46 Healthy Preoperative, Treatment naïve N Y 100 80930 5083171100 1899395790 37% 21821 1678 CGPLH47 Healthy Preoperative, Treatment naïve N Y 100 80930 6016388500 2062392156 34% 23459 1431 CGPLH48 Healthy Preoperative, Treatment naïve N Y 100 80930 4958945900 1809825992 36% 20702 1698 CGPLH49 Healthy Preoperative, Treatment naïve N Y 100 80930 7953812200 2511365904 32% 27006 1440 CGPLH50 Healthy Preoperative, Treatment naïve N Y 100 80930 6989407600 2561288100 37% 29177 2591 CGPLH51 Healthy Preoperative, Treatment naïve N Y 100 80930 7862073200 2525091396 32% 29999 1293 CGPLH52 Healthy Preoperative, Treatment naïve N Y 100 80930 6939636800 2397922699 35% 27029 2501 CGPLH54 Healthy Preoperative, Treatment naïve N Y 100 80930 10611934700 2290823134 22% 27175 3306 CGPLH55 Healthy Preoperative, Treatment naïve N Y 100 80930 9912569200 2521962244 25% 27082 3161 CGPLH56 Healthy Preoperative, Treatment naïve N Y 100 80930 5777591900 2023874863 35% 22916 1301 CGPLH57 Healthy Preoperative, Treatment naïve N Y 100 80930 9234904800 1493926244 16% 15843 1655 CGPLH59 Healthy Preoperative, Treatment naïve N Y 100 80930 9726052100 2987875484 31% 35427 2143 CGPLH63 Healthy Preoperative, Treatment naïve N Y 100 80930 8696405000 252157475

29% 26689 1851 CGPLH64 Healthy Preoperative, Treatment naïve N Y 100 80930 5438852600 996198502 18% 11477 1443 CGPLH75 Healthy Preoperative, Treatment naïve Y N 100 80930 3446444000 1505718480 44% 17805 3016 CGPLH76 Healthy Preoperative, Treatment naïve N Y 100 80930 7499116400 3685762725 49% 43682 4643 CGPLH77 Healthy Preoperative, Treatment naïve Y N 100 80930 6512408400 2537359345 39% 30280 3131 CGPLH78 Healthy Preoperative, Treatment naïve N Y 100 80930 7642949300 3946069680 52% 46316 5358 CGPLH79 Healthy Preoperative, Treatment naïve N Y 100 80930 7785475700 3910639227 50% 45280 6714 CGPLH80 Healthy Preoperative, Treatment naïve N Y 100 80930 7918361500 3558236955 45% 42171 5062 CGPLH81 Healthy Preoperative, Treatment naïve Y N 100 80930 6646268900 3112369850 47% 37119 3678 CGPLH82 Healthy Preoperative, Treatment naïve N Y 100 80930 7744065000 3941700596 51% 46820 5723 CGPLH83 Healthy Preoperative, Treatment naïve Y N 100 80930 6957686000 1447503106 21% 17280 2875 CGPLH84 Healthy Preoperative, Treatment naïve Y N 100 80930 8326493200 3969908122 48% 47464 3647 CGPLH86 Healthy Preoperative, Treatment naïve N Y 100 80930 8664194700 4470145091 52% 53398 5094 CGPLH90 Healthy Preoperative, Treatment naïve N Y 100 80930 751

078800 3841504088 51% 45907 4414 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 5659546100 1721618955 30% 20587 6025 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 6199049700 2563659840 41% 30728 6514 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 5864396500 1194237002 20% 14331 3952 CGPLLU13 Lung Cancer Pre-treatment, Day −2 Y N 100 80930 5080197700 1373550586 27% 16480 5389 CGPLLU14 Lung Cancer Pre-treatment, Day −38 N Y 100 80930 8668655700 3980731089 46% 48628 3148 CGPLLU14 Lung Cancer Pre-treatment, Day −16 N Y 100 80930 8271043600 4105092738 50% 50152 4497 CGPLLU14 Lung Cancer Pre-treatment, Day −3 N Y 100 80930 7149809200 3405754720 48% 40382 6170 CGPLLU14 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 6556332200 3289504484 50% 39004 4081 CGPLLU14 Lung Cancer Post-treatment, Day 0.33 N Y 100 80930 7410378300 3464236558 47% 41108 4259 CGPLLU14 Lung Cancer Post-treatment, Day 7 N Y 100 80930 7530190700 3752054349 50% 45839 2469 CGPLLU144 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8716827400 4216576624 48% 49370 10771 CGPLLU146 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8506844200 4195033049 49% 49084 6968 CGPLLU147 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7416300600 3530746046 48% 41302 4691 CGPLLU161 Lung Cancer Preoperative, Treatment naïve N Y 100 80930 7789148700 3280139772 42% 38568 12229 CGPLLU162 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7625462000 3470147667 46% 40918 10099 CGPLLU163 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8019293200 3946533983 49% 46471 12108 CGPLLU164 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8110030900 3592748235 44% 42161 6947 CGPLLU165 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8389514600 4147501817 49% 48770 8996 CGPLLU168 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7690630000 3868237773 50% 45625 9711 CGPLLU169 Lung Cancer Preoperative, Treatment naïve N Y 100 80930 9378353000 4800407624 51% 56547 10261 CGPLLU174 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7481844600 3067532518 41% 36321 6137 CGPLLU175 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8532324200 4002541569 47% 47084 7862 CGPLLU176 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8143905000 4054098929 50% 47708 5588 CGPLLU177 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 8421611300 4197108809 50% 49476 8780 CGPLLU178 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8483124700 416957748

49% 48580 6445 CGPLLU179 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7774358700 3304915738 43% 38768 6862 CGPLLU180 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 8192813800 3937552475 48% 46498 6568 CGPLLU197 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7996779200 3082397881 39% 36381 5388 CGPLLU198 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7175247200 3545719100 49% 42008 6817 CGPLLU202 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 6840112800 3427820669 50% 40670 7951 CGPLLU203 Lung Cancer Preoperative, Treatment naïve N Y 100 80930 7468749900 3762726574 50% 44500 9917 CGPLLU204 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7445026400 3703545153 50% 44317 6856 CGPLLU205 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 9205429100 4350573991 47% 51627 9810 CGPLLU206 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 7397914600 3635210205 49% 43016 7124 CGPLLU207 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7133043900 3736258011 52% 44291 8499 CGPLLU208 Lung Cancer Preoperative, Treatment naïve Y Y 100 80930 7346976400 3855814032 52% 45782 8940 CGPLLU209 Lung Cancer Preoperative, Treatment naïve Y N 100 80930 6723337800 3362944595 50% 39531 11946 CGPLLU244 Lung Cancer Pre-treatment, Day −7 N Y 100 80930 8305560600 4182616104 50% 50851 7569 CGPLLU244 Lung Cancer Pre-treatment, Day −1 N Y 100 80930 7739951100 3788487116 49% 45925 8552 CGPLLU244 Lung Cancer Post-treatment, Day 6 N Y 100 80930 8061928000 4225322272 52% 51279 8646 CGPLLU244 Lung Cancer Post-treatment, Day 62 N Y 100 80930 8894936700 443796263

50% 53862 7361 CGPLLU245 Lung Cancer Pre-treatment, Day −32 N Y 100 80930 7679235200 3935822054 51% 47768 7266 CGPLLU245 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 8985252500 482426833

54% 58338 10394 CGPLLU245 Lung Cancer Post-treatment, Day 7 N Y 100 80930 8518229300 4480236927 53% 54083 10125 CGPLLU245 Lung Cancer Post-treatment, Day 21 N Y 100 80930 9031131000 4824738475 53% 58313 10598 CGPLLU246 Lung Cancer Pre-treatment, Day −21 N Y 100 80930 8520360800 3509660305 41% 42349 8086 CGPLLU246 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 5451467800 2826351657 52% 34243 8256 CGPLLU246 Lung Cancer Post-treatment, Day 9 N Y 100 80930 8137616600 4135036174 51% 50121 6466 CGPLLU246 Lung Cancer Post-treatment, Day 42 N Y 100 80930 8385724600 4413323333 53% 53495 7303 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 3254777700 3016326208 48% 36164 12138 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6185331000 8087883231 50% 37003 8388 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6274540300 2861143666 46% 34308 6817 CGPLLU264 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 5701274000 1241270938 22% 14886 4273 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6091276800 2922585558 48% 35004 7742 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 8430107900 2945953499 46% 35219 8574 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5869510300 2792208995 48% 33423 8423 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5884330900 2588386038 44% 30977 9803 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5807524900 2347651479 40% 28146 5793 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6064269800 2086938782 34% 24994 6221 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6785913900 3458588505 51% 41432 7765 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6513702000 2096370387 32% 25142 6598 CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930

610761200 2576886

19 39% 31095 4485 CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6156102000 2586081726 42% 30714 5309 CGPLLU267 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6180799700 2013434756 33% 23902 3885 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6221168600 1499602843 24% 17799 6098 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5353961600 1698331125 32% 20094 5252 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5831612800 1521114956 26% 18067 6210 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6229704000 1481468974 24% 17608 4633 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6134366400 1351029627 22% 16170 7024 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6491884900 1622578435 25% 19433 5792 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 5742881200 2349421128 41% 28171 5723 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 5503999300 1695782705 31% 20320 5907 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6575907000 3002048491 46% 35997 5445 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6204350900 3016077187 49% 36162 5704 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 5997724300 2989608757 50% 35873 6228 CGPLLU43 Lung Cancer Pre-treatment, Day −1 Y N 100 80930 6026261500 2881177658 48% 34568 7221 CGPLLU86 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 8222093400 3523035056 43% 41165 3614 CGPLLU86 Lung Cancer Post-treatment, Day 0.5 N Y 100 80930 8305719500 4271264008 51% 49508 6681 CGPLLU86 Lung Cancer Post-treatment, Day 7 N Y 100 80930 6787785300 3443658418 51% 40132 3643 CGPLLU86 Lung Cancer Post-treatment, Day 17 N Y 100 80930 6213229400 3120325926 50% 36413 3560 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 7252433900 3621678746 50% 42719 8599 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 7679995800 4004738253 52% 46951 6387 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6509178000 3316053733 51% 39274 2661 CGPLLU89 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 7662496600 3781536306 49% 44097 7909 CGPLLU89 Lung Cancer Post-treatment, Day 7 N Y 100 80930 7005599500 3339612564 48% 38977 5034 CGPLLU89 Lung Cancer Post-treatment, Day 22 N Y 100 80930 8325998600 3094796789 37% 36061 2822 CGPLOV10 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7073534200 3402308123 48% 39820 4059 CGPLOV11 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 6924062200 3324593050 48% 38796 7185 CGPLOV12 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930

552080100 3181854993 49% 37340 6114 CGPLOV13 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 6796755500 3264897084 48% 38340 7931 CGPLOV14 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7856573900 3408425065 43% 39997 7712 CGPLOV15 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7239201500 3322285607 46% 38953 6644 CGPLOV16 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8570755900 4344288233 51% 51009 11947 CGPLOV17 Ovarian Cancer Preoperative, Treatment naïve Y N 100 80930

910310400 2805243492 41% 32828 4307 CGPLOV18 Ovarian Cancer Preoperative, Treatment naïve Y N 100 80930 8173037600 4064432407 50% 47714 5182 CGPLOV19 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7732198900 3672564399 47% 43020 11127 CGPLOV20 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 7559602000 3678700179 49% 43230 4872 CGPLOV21 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 8949032900 4616255499 52% 54012 12777 CGPLOV22 Ovarian Cancer Preoperative, Treatment naïve Y Y 100 80930 8680136500 4049934586 47% 46912 9715 CGPLOV23 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 6660696600 3422631774 51% 40810 9460 CGPLOV24 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8634287200 4272258165 49% 50736 8689 CGPLOV25 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 6978295000 3390206388 49% 40188 5856 CGPLOV26 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7041038300 3728879661 53% 44341 8950 CGPLOV28 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7429236900 3753051715 51% 45430 4155 CGPLOV31 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8981384000 4621838729 51% 55429 5458 CGPLOV32 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9344536800 4737698323 51% 57234 6165 CGPLOV37 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8158083200 4184432898 51% 50648 6934 CGPLOV38 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8654435400 4492987085 52% 53789 6124 CGPLOV40 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9868640700 4934400809 50% 59049 7721 CGPLOV41 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7689013600 3861448829 50% 46292 4469 CGPLOV42 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9836516300 4864154366 49% 58302 7632 CGPLOV43 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8756507100 4515479918 52% 54661 4310 CGPLOV44 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7576310800 4120933322 54% 49903 4969 CGPLOV46 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 9346036300 5037820346 54% 61204 3927 CGPLOV47 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 10880620200 5491357828 50% 66363 6895 CGPLOV48 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 7658787800 3335991337 44% 40332 4066 CGPLOV49 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 10076208000 5519656698 55% 67117 5097 CGPLOV50 Ovarian Cancer Preoperative, Treatment naïve N Y 100 80930 8239290400 4472380276 54% 54150 3836 CGPLPA118 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 9094827600 4828332902 53% 57021 4802 CGPLPA122 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7303323100 3990160379 55% 47240 7875 CGPLPA124 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7573482800 3965807442 52% 46388 8658 CGPLPA126 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7904953600 4061463168 51% 47812 10498 CGPLPA128 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7249238300 2244188735 31% 26436 3413 CGPLPA129 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7559858900 4003725804 53% 47182 5733 CGPLPA130 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 6973946500 1247144905 18% 14691 1723 CGPLPA131 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7226237900 3370664342 47% 39661 5054 CGPLPA134 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7268866100 3754945844 52% 44306 7023 CGPLPA13

Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7476690700 4073978408 54% 48134 5244 CGPLPA140 Bile Duct Cancer Preoperative, Treatment naïve N Y 100 80930 7364654600 3771765342 51% 44479 7080 CGST102 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 5715504500 2644902854 46% 31309 4503 CGST110 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 9179291500 4298269268 47% 51666 3873 CGST114 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 7151572200 3254967293 46% 38496 4839 CGST13 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6449701500 3198545984 50% 38515 6731 CGST141 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6781001300 3440927391 51% 40762 5404 CGST16 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6396470600 293138028

46% 35354 8148 CGST18 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930

647324000 3138967777 47% 37401 4992 CGST28 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6288486100 2884997993 46% 34538 2586 CGST30 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6141213100 3109994564 51% 37194 2555 CGST32 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6969139300 309912046

44% 36726 3935 CGST33 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6560309400 3168371917 48% 37916 4597 CGST39 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 7043791400 2992801875 42% 35620 6737 CGST41 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6975053100 3224065662 46% 38300 4016 CGST45 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6130812200 2944524278 48% 35264 4745 CGST47 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 5961400000 3083523351 52% 37008 3112 CGST48 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930

418652700 1497230327 23% 17782 2410 CGST58 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 5818344500 1274708429 22% 15281 2924 CGST80 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 6368064600 3298497188 52% 39692 5280 CGST81 Gastric Cancer Preoperative, Treatment naïve N Y 100 80930 8655691400 1519121452 18% 17988 6419

indicates data missing or illegible when filed

TABLE 3 APPENDIX C: Targeted cfDNA fragment analyses in cancer patients Patient Stage at Amino Acid Mutation Patient Type Diagnosis Alteration Type Gene (Protein) Nucleotide Type CGCRC291 Colorectal IV Tumor-derived STK11 39R>C chr19_1207027-1207027_C_T Substitution Cancer CGCRC291 Colorectal IV Tumor-derived TP53 272V>M chr17 7577124-7577124 C T Substitution Cancer CGCRC291 Colorectal IV Tumor-derived TP53 167Q>X chr17_7578431-7578431_G_A Substitution Cancer CGCRC291 Colorectal IV Tumor-derived KRAS 12G>A chr12_25398284-25398284_C_G Substitution Cancer CGCRC291 Colorectal IV Tumor-derived APC 1260Q>X chr5_112175069-112175069_C_T Substitution Cancer CGCRC291 Colorectal IV Tumor-derived APC 1450R>X chr5_112175639-112175639_C_T Substitution Cancer CGCRC291 Colorectal IV Tumor-derived PIK3CA 542E>K chr3_178936082-178936082_G_A Substitution Cancer CGCRC292 Colorectal IV Tumor-derived KRAS 146A>V chr12_25378561-25378561_G_A Substitution Cancer CGCRC292 Colorectal IV Tumor-derived CTNNB1 41T>A chr3_41266124-41266124_A_G Substitution Cancer CGCRC292 Colorectal IV Germline EGFR 2284−4C>G chr7_55248982-55248982_C_G Substitution Cancer CGCRC293 Colorectal IV Tumor-derived TP53 176C>S chr17_7578404-7578404_A_T Substitution Cancer CGCRC294 Colorectal II Tumor-derived APC 213R>X chr5_112116592-112116592_C_T Substitution Cancer CGCRC294 Colorectal II Tumor-derived APC 1367Q>X chr5_112175390-112175390_C_T Substitution Cancer CGCRC295 Colorectal IV Tumor-derived PDGFRA 49+4C>T chr4_55124988-55124988_C_

Substitution Cancer CGCRC295 Colorectal IV Hematopoietic IDH1 104G>V chr2_209113196-209113196_C_A Substitution Cancer CGCRC296 Colorectal II Germline EGFR 922E>K chr7_55266472-55266472_G_A Substitution Cancer CGCRC297 Colorectal III Germline KIT 18L>F chr4_55524233-55524233_C_

Substitution Cancer CGCRC298 Colorectal II Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGCRC298 Colorectal II Hematopoietic DNMT3A 714S>C chr2_25463541-25463541_G_C Substitution Cancer CGCRC298 Colorectal II Tumor-derived PIK3CA 414G>V chr3_178927478-178927478_G_T Substitution Cancer CGCRC299 Colorectal I Hematopoietic DNMT3A 735Y>C chr2_25463289 25463289_T_C Substitution Cancer CGCRC299 Colorectal I Hematopoietic DNMT3A 710C>S chr2_25463553-25463553_C_G Substitution Cancer CGCRC300 Colorectal I Hematopoietic DNMT3A 720R>G chr2_25463524-25463524_G_C Substitution Cancer CGCRC301 Colorectal I Tumor-derived ATM 2397Q>X chr11_108199847-108199847_C_T Substitution Cancer CGCRC302 Colorectal II Tumor-derived TP53 141C>Y chr17_7578508-7578508_C_T Substitution Cancer CGCRC302 Colorectal II Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution Cancer CGCRC303 Colorectal III Tumor-derived TP53 173V>L chr17_7578413-7578413_C_A Substitution Cancer CGCRC303 Colorectal III Hematopoietic DNMT3A 755F>3 chr2_25463229-25463229_A_G Substitution Cancer CGCRC303 Colorectal III Hematopoietic DNMT3A 2173+1G>A chr2_25463508-25463508_C_

Substitution Cancer CGCRC304 Colorectal II Tumor-derived EGFR 1131T>S chr7_55273068-55273068_A_

Substitution Cancer CGCRC304 Colorectal II Tumor-derived ATM 3077+1G>A chr11_108142134-108142134_G_A Substitution Cancer CGCRC304 Colorectal II Hematopoietic ATM 3008R>C chr11_108236086-108236086_C_T Substitution Cancer CGCRC305 Colorectal II Tumor-derived GNA11 213R>Q chr19_3118954-3118954_G_A Substitution Cancer CGCRC305 Colorectal II Tumor-derived TP53 273R>H chr17_7577120-7577120_C_T Substitution Cancer CGCRC306 Colorectal II Tumor-derived TP53 196R>X chr17_7578263-7578263_G_A Substitution Cancer CGCRC306 Colorectal II Tumor-derived CDKN2A 107R>C chr9_21971039-21971039_G_A Substitution Cancer CGCRC306 Colorectal II Tumor-derived KRAS 61Q>K chr12_25380277-25380277_G_T Substitution Cancer CGCRC306 Colorectal II Germline PDGFRA 200T>S chr4_55130065-55130065_C_G Substitution Cancer CGCRC306 Colorectal II Tumor-derived EGFR 618H>R chr7_55233103-55233103_A_G Substitution Cancer CGCRC306 Colorectal II Tumor-derived PIK3CA 545E>A chr3_178936092-178936092_A_C Substitution Cancer CGCRC3G6 Colorectal II Germline ERBB4 1155R>X chr2_212251596-212251596_G_A Substitution Cancer CGCRC307 Colorectal II Tumor-derived JAK2 805L>V chr9_5080662-5080662_C_G Substitution Cancer CGCRC307 Colorectal II Tumor-derived SMARCB1 5C1−2A>G chr22_24145480-24145480_A_G Substitution Cancer CGCRC307 Colorectal II Tumor-derived GNAS 201R>C chr20_57484420-57484420_C_T Substitution Cancer CGCRC307 Colorectal II Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution Cancer CGCRC307 Colorectal II Tumor-derived FBXW7 465R>C chr4_153249365-153249385_G_A Substitution Cancer CGCRC307 Colorectal II Tumor-derived ER8B4 17A>V chr2_213403205-213403205_G_A Substitution Cancer CGCRC308 Colorectal III Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGCRC308 Colorectal III Germline EGFR 848P>L chr7_55259485-55259485_C_

Substitution Cancer CGCRC308 Colorectal III Tumor-derived APC 1480Q>X chr5_112175729-112175729_C_T Substitution Cancer CGCRC309 Colorectal III Tumor-derived AKT1 17E>K chr14_105246551-105246551_C_T Substitution Cancer CGCRC309 Colorectal III Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution Cancer CGCRC310 Colorectal II Tumor-derived KRAS 12G>V chr12_25398284-25398284_C_A Substitution Cancer CGCRC310 Colorectal II Tumor-derived APC 1513E>X chr5_112175828-112175828_G_T Substitution Cancer CGCRC310 Colorectal II Tumor-derived APC 1521E>X chr5_112175852-112175352_G_T Substitution Cancer CGCRC311 Colorectal I Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGCRC312 Colorectal III Tumor-derived APC 960S>X chr5_112174170-112174170_C_G Substitution Cancer CGCRC312 Colorectal III Tumor-derived NRAS 61Q>K chr1_115256530-115256530_G_T Substitution Cancer CGCRC313 Colorectal III Tumor-derived KRAS 12G>S chr12_25398285-25398285_C_T Substitution Cancer CGCRC313 Colorectal III Tumor-derived APC 876R>X chr5_112173917-112173917_C_T Substitution Cancer CGCRC314 Colorectal I Tumor-derived KRAS 12G>D chr12_25398284-25398284_C_T Substitution Cancer CGCRC314 Colorectal I Hematopoietic DNMT3A 738L>Q chr2_25463280-25463280_A_

Substitution Cancer CGCRC314 Colorectal I Tumor-derived APC 1379E>X chr5_112175426-112175426_G_T Substitution Cancer CGCRC315 Colorectal III Tumor-derived NRAS 12G>D chr1_115258747-115258747_C_T Substitution Cancer CGCRC315 Colorectal III Tumor-derived FBXW7 505R>3 chr4_153247289-153247289_G_A Substitution Cancer CGCRC316 Colorectal III Tumor-derived TP53 245G>S chr17_7577548-7577548_C_T Substitution Cancer CGCRC316 Colorectal III Tumor-derived CDKN2A 1M>R chr9_21974825-21974825_A_C Substitution Cancer CGCRC316 Colorectal III Tumor-derived CTNNB1 37S>C chr3_41266113-41266113_C_G Substitution Cancer CGCRC316 Colorectal III Tumor-derived EGFR 2702−3C>T chr7_55266407-55266407_C_

Substitution Cancer CGCRC316 Colorectal III Hematopoietic ATM 3008R>P chr11_108236087-108236087_G_C Substitution Cancer CGCRC317 Colorectal III Tumor-derived TP53 220Y>C chr17_7578190-7578190_T_C Substitution Cancer CGCRC317 Colorectal III Tumor-derived ATM 1026W>R chr11_108142132-108142132_T_C Substitution Cancer CGCRC317 Colorectal III Tumor-derived APC 216R>X chr5_112128143-112128143_C_T Substitution Cancer CGCRC318 Colorectal I Hematopoietic DNMT3A 698W>X chr2_25463589-25463589_C_

Substitution Cancer CGCRC320 Colorectal I Germline KIT 18L>F chr4_55524233-55524233_C_

Substitution Cancer CGCRC320 Colorectal I Tumor-derived ERBB4 78R>W chr2_212989479-212989479_G_A Substitution Cancer CGCRC321 Colorectal I Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution Cancer CGCRC321 Colorectal I Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGCRC321 Colorectal I Germline EGFR 511S>Y chr7_55229225-55229225_C_A Substitution Cancer CGCRC332 Colorectal IV Tumor-derived TP53 125T>R chr17_7579313-7579313_G_C Substitution Cancer CGCRC333 Colorectal IV Tumor-derived TP53 673−2A>G chr17_7577610-7577610_T_C Substitution Cancer CGCRC333 Colorectal IV Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution Cancer CGCRC333 Colorectal IV Tumor-derived ERBB4 691E>A chr2_212495194-212495194_T_G Substitution Cancer CGCRC334 Colorectal IV Tumor-derived TP53 245G>S chr17_7577548-7577548_C_T Substitution Cancer CGCRC334 Colorectal IV Germline EGFR 638T>M chr7_55238900-55238900_C_

Substitution Cancer CGCRC334 Colorectal IV Tumor-derived PIK3CA 104P>R chr3_178916924-178916924_C_G Substitution Cancer CGCRC335 Colorectal IV Tumor-derived BRAF 600V>E chr7_140453136-140453136_A_T Substitution Cancer CGCRC336 Colorectal IV Tumor-derived TP53 175R>H chr17_7578406-7578406_C_T Substitution Cancer CGCRC336 Colorectal IV Tumor-derived KRAS 12G>V chr12_25398284-25398284_C_A Substitution Cancer CGCRC336 Colorectal IV Turner-derived APC 1286E>X chr5_112175147-112175147_G_T Substitution Cancer CGCRC337 Colorectal IV Tumor-derived STK11 734+2T>A chr19_1220718-1220718_T_A Substitution Cancer CGCRC337 Colorectal IV Germline APC 485M>I chr5_112162851-112162851_G_A Substitution Cancer CGCRC338 Colorectal IV Tumor-derived KRAS 12G>D chr12_25398284-25398284_C_T Substitution Cancer CGCRC339 Colorectal IV Tumor-derived KRAS 13G>D chr12_25393281-25398281_C_T Substitution Cancer CGCRC339 Colorectal IV Tumor-derived APC 876R>X chr5_112173917-112173917_C_T Substitution Cancer CGCRC339 Colorectal IV Tumor-derived PIK3CA 407C>F chr3_178927457-178927457_G_T Substitution Cancer CGCRC339 Colorectal IV Tumor-derived PIK3CA 1047H>L chr3_178952085-178952085_A_T Substitution Cancer CGCRC340 Colorectal IV Tumor-derived TP53 196R>X chr17_7578263-7578263_G_A Substitution Cancer CGCRC340 Colorectal IV Tumor-derived APC 1306E>X chr5_112175207-112175207_G_T Substitution Cancer CGPLBR38 Breast I Tumor-derived TP53 241S>P chr17_7577560-7577560_A_G Substitution Cancer CGPLBR40 Breast III Germline AR 392P>R chrX_66766163-66766163_C_G Substitution Cancer CGPLBR44 Breast III Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGPLBR44 Breast III Hematopoietic DNMT3A 705I>T chr2_25463568-25463568_A_G Substitution Cancer CGPLBR44 Breast III Tumor-derived PDGFRA 859V>M chr4_55153609-55153609_G_A Substitution Cancer CGPLBR48 Breast II Germline ALK 1231R>Q chr2_29436901-29436901_C_

Substitution Cancer CGPLBR48 Breast II Tumor-derived EGFR 669R>Q chr7_55240762-55240762_G_A Substitution Cancer CGPLBR55 Breast III Hematopoietic DNMT3A 743P>S chr2_25463266-25463266_G_A Substitution Cancer CGPLBR55 Breast III Tumor-derived GNAS 201R>H chr20_57484421-57484421_G_A Substitution Cancer CGPLBR55 Breast III Tumor-derived PIK3CA 345N>K chr3_178921553-178921553_T_A Substitution Cancer CGPLBR63 Breast II Germline FGFR3 403K>E chr4_1806188-1806188_A_G Substitution Cancer CGPLBR67 Breast III Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGPLBR67 Breast III Tumor-derived PIK3CA 545E>K chr3_178936091-178936091_G_A Substitution Cancer CGPLBR67 Breast III Tumor-derived ERBB4 1000D>A chr2_212285302-212285302_T_G Substitution Cancer CGPLBR69 Breast II Hematopoietic DNMT3A 774E>V chr2_25463172-25463172_T_A Substitution Cancer CGPLBR69 Breast II Germline CTNNB1 30Y>S chr3_41266092-41266092_A_C Substitution Cancer CGPLBR69 Breast II Germline IDH1 231Y>N chr2_209108158-209108158_A_T Substitution Cancer CGPLBR70 Breast II Tumor-derived ATM 2832R>H chr11_108216546-108216546_G_A Substitution Cancer CGRLBR70 Breast II Germline APC 1577E>D chr5_112176022-112176022_A_C Substitution Cancer CGPLBR71 Breast II Tumor-derived TP53 273R>H chr17_7577120-7577120_C_T Substitution Cancer CGPLBR72 Breast II Germline APC 1532D>G chr5_112175886-112175886_A_G Substitution Cancer CGPLBR73 Breast II Tumor-derived ALK 708S>P chr2_29474053-29474053_A_G Substitution Cancer CGPLBR73 Breast II Germline ERBB4 158A>E chr2_212652833-212652833_G_T Substitution Cancer CGPLBR74 Breast II Germline AR 20+1G>T chrX_66788865-66788865_G_T Substitution Cancer CGPLBR75 Breast II Tumor-derived PIK3CA 1047H>R chr3_178952085-178352085_A_G Substitution Cancer CGPLBR76 Breast II Germline KDR 1290S>N chr4_55946310-55946310_C_

Substitution Cancer CGPLBR76 Breast II Tumor-derived PIK3CA 1047H>R chr3_178952085-178952085_A_G Substitution Cancer CGPLBR77 Breast III Tumor-derived PTEN 170S>I chr10_89711891-89711891_G_T Substitution Cancer CGPLBR80 Breast II Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution Cancer CGPLBR83 Breast II Germline AR 728N>D chrX_66937328-66937328_A_G Substitution Cancer GGPLBR83 Breast II Tumor-derived ATM 322E>K chr11_108117753-108117753_G_A Substitution Cancer CGPLBR83 Breast II Germline ERBB4 539Y>S chr2_212543783 212543783_T_G Substitution Cancer CGPLBR86 Breast II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution Cancer CGPLBR86 Breast II Germline SMARCB1 795+3A>G chr22_24159126-24159126_A_G Substitution Cancer CGPLBR87 Breast II Tumor-derived JAK2 215R>X chr9_5054591-5054591_C_T Substitution Cancer CGPLBR87 Breast II Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGPLBR87 Breast II Tumor-derived SMAD4 496R>C chr18_48304664-48604664_C_T Substitution Cancer CGPLBR87 Breast II Germline AR 651S>N chrX_66931310-66931310_G_A Substitution Cancer CGPLBR88 Breast II Tumor-derived CDK6 51E>K chr7_92462487-92462487_G_T Substitution Cancer CGPLBR88 Breast II Germline APC 1125V>A chr5_112174665-112174665_T_C Substitution Cancer CGPLBR92 Breast II Tumor-derived TP53 257L>P chr17_7577511-7577511_A_G Substitution Cancer CGPLBR96 Breast II Tumor-derived TP53 213R>X chr17.1a: 7578212-7576212_G_A Substitution Cancer CGPLBR96 Breast II Hematopoietic DNMT3A 531D>G chr2_25467484-25467434_T_G Substitution Cancer CGPLBR96 Breast II Tumor-derived AR 13R>Q chrX_66765026-66765026_G_A Substitution Cancer CGPLBR97 Breast II Hematopoietic DNMT3A 882R>H chr2_25457242-25457242_C_

Substitution Cancer CGPLBR97 Breast II Germline PDGFRA 401A>D chr4_55136880-55136880_C_A Substitution Cancer CGPLBR97 Breast II Tumor-derived GNAS 201R>H chr20_57484421-57484421_G_A Substitution Cancer CGPLLU144 Lung II Tumor-derived TP53 241S>F chr17_7577559-7577559_G_A Substitution Cancer CGPLLU144 Lung II Tumor-derived KRAS 12G>C chr12_25398285-25398285_C_A Substitution Cancer CGPLLU144 Lung II Tumor-derived EGFR 373P>S chr7_55224336-55224336_C_

Substitution Cancer CGPLLU144 Lung II Tumor-derived ATM 292P>L chr11_108115727-108115727_C_T Substitution Cancer CGPLLU144 Lung II Tumor-derived PIK3CA 545E>K chr3_178936091 178936091_G_A Substitution Cancer CGPLLU144 Lung II Tumor-derived ERBB4 426R>K chr2_212568841-212568841_C_T Substitution Cancer CGPLLU146 Lung II Hematopoietic JAK2 617V>F chr9_5073770-5073770_G_T Substitution Cancer CGPLLU146 Lung II Tumor-derived TP53 282R>P chr17_7577093-7577093_C_G Substitution Cancer CGPLLU146 Lung II Hematopoietic DNMT3A 737L>H chr2_25463283-25463283_A_

Substitution Cancer CGPLLU146 Lung II Tumor-derived RB1 861+2T>C chr13_48937095-48937095_T_C Substitution Cancer CGPLLU146 Lung II Tumor-derived ATM 581L>F chr11_108122699-108122699_A_T Substitution Cancer CGPLLU147 Lung III Tumor-derived TP53 248R>Q chr17_7577538-7577538_C_T Substitution Cancer CGPLLU147 Lung III Tumor-derived TP53 201L>X chr17_7573247-7578247_A_T Substitution Cancer CGPLLU147 Lung III Tumor-derived ALK 1537G>E chr2_29416343-29416343_C_

Substitution Cancer CGPLLU147 Lung III Germline PDGFRA 200T>S chr4_55130065-55130065_C_G Substitution Cancer CGPLLU162 Lung II Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution Cancer CGPLLU162 Lung II Tumor-derived EGFR 858L>R chr7_55259515-55259515_T_G Substitution Cancer CGPLLU162 Lung II Tumor-derived BRAF 354R>Q chr7_140494187-140494187_C_T Substitution Cancer CGPLLU163 Lung II Tumor-derived CDKN2A 12S>L chr9_21974792-21974792_G_A Substitution Cancer CGPLLU163 Lung II Hematopoietic DNMT3A 528Y>D chr2_25467494-25467494_A_C Substitution Cancer CGPLLU164 Lung II Tumor-derived STK11 216S>Y chr19_1220629-1220629_C_A Substitution Cancer CGPLLU164 Lung II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution Cancer CGPLLU164 Lung II Tumor-derived GNA11 606−3C>

chr19_3118919-3118919_C_T Substitution Cancer CGPLLU164 Lung II Tumor-derived TP53 278P>S chr17_7577106-7577106_G_A Substitution Cancer CGPLLU164 Lung II Tumor-derived TP53 161A>S chr17_7578449-7578449_C_A Substitution Cancer CGPLLU164 Lung II Tumor-derived TP53 160M>I chr17_7578450-7578450_C_A Substitution Cancer CGPLLU164 Lung II Tumor-derived ERBB4 1299P>L chr2_212248371-212248371_G_A Substitution Cancer CGPLLU164 Lung II Tumor-derived ERBB4 253N>S chr2_212587243-212587243_T_C Substitution Cancer CGPLLU165 Lung II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution Cancer CGPLLU165 Lung II Tumor-derived GNAS 201R>H chr20_57484421-57484421_G_A Substitution Cancer CGPLLU168 Lung I Tumor-derived TP53 136Q>X chr17.1a: 7578524-7578524_G_A Substitution Cancer CGPLLU168 Lung I Hematopoietic DNMT3A 736R>S chr2_25463287-25463287_G_T Substitution Cancer CGPLLU168 Lung I Tumor-derived EGFR 858L>R chr7.1a: 55259515-55259515_T_G Substitution Cancer CGPLLU174 Lung I Tumor-derived STK11 597+1G>T chr19_1220505-1220505_G_T Substitution Cancer CGPLLU174 Lung I Tumor-derived JAK2 160D>Y chr9_5050695-5050695_G_T Substitution Cancer CGPLLU174 Lung I Tumor-derived KRAS 12G>C chr12_25398285-25398285_C_A Substitution Cancer CGPLLU174 Lung I Hematopoietic DNMT3A 891R>W chr2_25457216-25457216_G_A Substitution Cancer CGPLLU174 Lung I Hematopoietic DNMT3A 715I>M chr2_25463537-25463537_G_C Substitution Cancer CGPLLU175 Lung I Tumor-derived TP53 179H>R chr17_7578394-7578394_T_C Substitution cancer CGPLLU175 Lung I Hematopoietic DNMT3A 2598−1G>A chr2_25457290-25457290_C_

Substitution Cancer CGPLLU175 Lung I Hematopoietic DNMT3A 755F>L chr2_25463230-25463230_A_G Substitution Cancer CGPLLU175 Lung I Germline ATM 337R>C chr11_108117798-108117798_C_T Substitution Cancer CGPLLU175 Lung I Tumor-derived ERBB4 941Q>X chr2_212288925-212288925_G_A Substitution Cancer CGPLLU176 Lung I Hematopoietic DNMT3A 750P>S chr2_25463245-25463245_G_A Substitution Cancer CGPLLU176 Lung I Hematopoietic DNMT3A 735Y>C chr2_25463239-25463239_T_C Substitution Cancer CGPLLU177 Lung II Tumor-derived KRAS 12G>V chr12_25398284-25398284_C_A Substitution Cancer CGPLLU177 Lung II Hematopoietic DNMT3A 897V>G chr2_25457197-25457197_A_C Substitution Cancer CGPLLU177 Lung II Hematopoietic DNMT3A 882R>C chr2_25457243-25457243_G_A Substitution Cancer CGPLLU177 Lung II Hematopoietic DNMT3A 2173+1G>A chr2_25463508-25463508_C_

Substitution Cancer CGPLLU178 Lung I Tumor-derived CDH1 251T>M chr16_68844164-68844164_C_T Substitution Cancer CGPLLU178 Lung I Tumor-derived PIK3CA 861Q>X chr3_178947145-178947145_C_T Substitution Cancer CGPLLU179 Lung I Hematopoietic DNMT3A 879N>D chr2_25457252-25457252_T_C Substitution Cancer CGPLLU179 Lung I Germline APC 2611T>I chr5_112179123-112179123_C_T Substitution Cancer CGPLLU180 Lung I Tumor-derived STK11 237D>Y chr19_1220691-1220691_G_T Substitution Cancer CGPLLU180 Lung I Tumor-derived TP53 293G>V chr17_7577068-7577060_C_A Substitution Cancer CGPLLU180 Lung I Tumor-derived TP53 282R>P chr17_577893-7577093_C_G Substitution Cancer CGPLLU180 Lung I Tumor-derived TP53 177P>L chr17.1a: 7578400-7578400_G_A Substitution Cancer CGPLLU180 Lung I Tumor-derived RB1 565S>X chr13_48955578-48955578_C_G Substitution Cancer CGPLLU197 Lung I Hematopoietic DNMT3A 882R>C chr2_25457243-25457243_G_A Substitution Cancer CGPLLU197 Lung I Hematopoietic DNMT3A 879N>D chr2_25457252-25457252_T_C Substitution Cancer CGPLLU198 Lung I Tumor-derived TP53 162I>N chr17_7576445-7576445_A_T Substitution Cancer CGPLLU198 Lung I Tumor-derived EGFR 858L>R chr7_25259515_55259515_T_G Substitution Cancer CGPLLU202 Lung I Tumor-derived EGFR 790T>M chr7.1a: 55249071-55249071_C_T Substitution Cancer CGPLLU202 Lung I Tumor-derived EGFR 868E>X chr7_55259544-55259544_G_T Substitution Cancer CGPLLU204 Lung I Tumor-derived KIT 956R>Q chr4_55604659-55604659_G_A Substitution Cancer CGPLLU205 Lung II Hematopoietic DNMT3A 736R>C chr2_25463287-25463287_G_A Substitution Cancer CGPLLU205 Lung II Hematopoietic DNMT3A 696Q>X chr2_25463596-25463596_G_A Substitution Cancer CGPLLU206 Lung III Tumor-derived TP53 672+1G>A chr17_7578176-7578176_C_T Substitution Cancer CGPLLU206 Luna III Tumor-derived TP53 131N>S chr17_7573538-7578538_T_C Substitution Cancer CGPLLU207 Lung II Tumor-derived TP53 376−1G>A chr17_573555-75785551_C_T Substitution Cancer CGPLLU207 Lung II Germline ALK 419F>L chr2_29606625-29606625_A_G Substitution Cancer CGPLLU207 Lung II Tumor-derived EGFR 790T>M chr7.1a: 552493071-55249071_C_T Substitution Cancer CGPLLU208 Lung II Tumor-derived TP53 250P>L chr17_7577532-7577532_G_A Substitution Cancer CGPLLU208 Lung II Germline EGFR 224R>H chr7_55220281-55228281_G_A Substitution Cancer CGPLLU208 Lung II Tumor-derived EGFR 858L>R chr7_513259515_55259515_T_G Substitution Cancer CGPLLU208 Lung II Tumor-derived MYC 98R>W chr8_128750755-126750755_C_T Substitution Cancer CGPLLU209 Lung II Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution Cancer CGPLLU209 Lung II Tumor-derived TP53 100Q>X chr17_7579389-7579389_G_A Substitution Cancer CGPLLU209 Lung II Tumor-derived CDKN2A 88E>X chr9_21971096-21971_OSS_C_A Substitution Cancer CGPLLU209 Lung II Tumor-derived PDGFRA 921A>T chr4_55155052_55155052_G_A Substitution Cancer CGPLLU209 Lung II Germline EGFR 567M>V chr7_55231493-55231493_A_G Substitution Cancer CGPLOV10 Ovarian I Tumor-derived TP53 342R>X chr17_7574003-7574003_G_A Substitution Cancer CGPLOV11 Ovarian IV Tumor-derived TP53 248R>Q chr17_7577538-7577538_C_T Substitution Cancer CGPLOV11 Ovarian IV Germline TP53 63A>V chr17_579499-7579499_G_A Substitution Cancer CGPLOV13 Ovarian IV Tumor-derived ALK 444W>C chr2_29551298-23551298_C_A Substitution Cancer CGPLOV13 Ovarian IV Germline PDGFRA 401A>D chr4_55136630-55136830_C_A Substitution Cancer CGPLOV13 Ovarian IV Tumor-derived KIT 135R>H chr4_55564516-555S4516_G_A Substitution Cancer CGPLOV14 Ovarian I Tumor-derived HNF1A 230E>K chr12_12143484-121431484_G_A Substitution Cancer CGPLOV15 Ovarian III Tumor-derived TP53 278P>S chr17_577106-7577106_G_A Substitution Cancer CGPLOV15 Ovarian III Tumor-derived EGFR 433H>D chr7_55225445_55225445_C_G Substitution Cancer CGPLOV17 Ovarian I Tumor-derived TP53 248R>Q chr17_7577539-7577538_C_T Substitution Cancer CGPLOV17 Ovarian I Germline PDGFRA 1071D>N chr4 55161380-55161380_G_A Substitution Cancer CGPLOV18 Ovarian I Germline AFC 1125V>A chr5_112174S65-112174365_T_C Substitution Cancer CGPLOV19 Ovarian II Germline FGFR3 403K>E chr4_1606163-1806183_A_G Substitution Cancer CGPLOV19 Ovarian II Tumor-derived TR53 273R>H chr17_577120-7577120_C_T Substitution Cancer CGPLOV19 Ovarian II Germline AR 176S>R chrX_66765516-66765516_C_A Substitution Cancer CGPLOV19 Ovarian II Tumor-derived APC 1378Q>X chr5_112175423-112175423_C_T Substitution Cancer CGPLOV20 Ovarian II Tumor-derived TP53 195I>T chr17_7578265-7578265_A_G Substitution Cancer CGPLOV20 Ovarian II Germline EGFR 253K>R chr7_55221714-55221714_A_G Substitution Cancer CGPLOV21 Ovarian IV Germline STK11 354F>L chr19_1223125-1223125_C_G Substitution Cancer CGPLOV21 Ovarian IV Tumor-derived TP53 275C>Y chr17_577114-7577114_C_T Substitution Cancer CGPLOV21 Ovarian IV Tumor-derived ERBB4 602S>T chr2_212530114_212530114_C_G Substitution Cancer CGPLOV22 Ovarian III Tumor-derived TP53 193H>P chr17_7573271-757S271_T_G Substitution Cancer CGPLOV22 Ovarian III Tumor-derived CTNNB1 41T>A chr3_41266124-41266124_A_G Substitution Cancer Wild-type Fragments 25th Minimum Percentile Mode Median Alteration Mutant cfDNA cfDNA cfDNA cfDNA Hotspot Detected Allele Distinct Fragment Fragment Fragment Fragment Patient Alteration in Tissue Fraction Coverage Size (bp) Size (bp) Size (bp) Size (bp) CGCRC291 No No 0.14% 11688 100 151 167 169 CGCRC291 Yes No 0.10% 11779 100 155 171 169 CGCRC291 Yes Yes 22.85% 11026 100 156 166 169 CGCRC291 Yes Yes 14.85% 7632 97 152 169 167 CGCRC291 No Yes 11.23% 7218 101 155 167 169 CGCRC291 Yes Yes 11.05% 10757 86 154 166 167 CGCRC291 Yes Yes 18.11% 5429 100 151 171 167 CGCRC292 Yes No 1.41% 6120 101 157 167 169 CGCRC292 Yes Yes 0.13% 10693 100 155 169 168 CGCRC292 NA Yes 31.99% 7587 97 158 166 171 CGCRC293 No No 0.35% 7672 95 159 168 170 CGCRC294 Yes Yes 0.14% 7339 84 155 166 167 CGCRC294 Yes Yes 0.13% 12054 89 159 167 170 CGCRC295 No No 0.45% 5602 101 157 164 170 CGCRC295 No Yes 0.34% 8330 100 157 166 169 CGCRC296 NA Yes 30.48% 8375 89 161 166 172 CGCRC297 NA Yes 41.39% 3580 102 159 164 170 CGCRC298 Yes Yes 0.08% 13032 100 159 168 171 CGCRC298 No No 0.11% 13475 93 158 169 170 CGCRC298 No No 0.55% 5815 100 158 168 169 CGCRC299 No Yes 0.30% 11995 100 154 164 165 CGCRC299 No Yes 0.12% 15363 96 151 166 164 CGCRC300 No No 0.15% 7487 100 162 179 173 CGCRC301 No No 0.21% 5881 100 156 169 169 CGCRC302 Yes Yes 0.05% 24784 84 153 165 164 CGCRC302 Yes Yes 0.12% 11763 95 154 165 165 CGCRC303 Yes Yes 0.08% 13967 95 159 169 171 CGCRC303 No No 0.21% 10167 81 160 169 172 CGCRC303 No No 0.17% 10845 100 160 169 172 CGCRC304 No No 0.22% 16168 90 153 167 164 CGCRC304 No No 0.27% 10502 100 152 165 163 CGCRC304 No Yes 0.43% 12987 101 154 165 165 CGCRC305 No Yes 0.11% 12507 100 159 169 171 CGCRC305 Yes No 0.19% 10301 100 156 168 166 CGCRC306 Yes No 0.12% 8594 101 157 165 169 CGCRC306 No Yes 8.02% 9437 90 159 167 171 CGCRC306 Yes Yes 7.30% 6090 100 152 163 166 CGCRC306 NA Yes 34.78% 4585 103 158 167 179 CGCRC306 No Yes 6.32% 7395 81 160 166 171 CGCRC306 Yes No 0.96% 4885 100 152 170 167 CGCRC3G6 NA Yes 38.70% 3700 100 159 168 171 CGCRC307 No No 0.56% 6860 100 158 170 170 CGCRC307 No Yes 0.34% 10065 95 157 168 169 CGCRC307 Yes Yes# 0.24% 7520 102 156 167 168 CGCRC307 Yes Yes 0.38% 8623 76 157 169 168 CGCRC307 Yes Yes 0.31% 10606 100 155 167 168 CGCRC307 No No 0.15% 13189 90 158 168 171 CGCRC308 Yes No 0.06% 16287 90 159 168 169 CGCRC308 NA Yes 27.69% 7729 100 160 164 170 CGCRC308 No Yes 0.11% 14067 92 157 170 169 CGCRC309 Yes Yes 2.70% 13036 85 157 170 169 CGCRC309 Yes Yes 3.00% 9084 101 157 166 168 CGCRC310 Yes Yes 0.13% 7393 100 153 165 164 CGCRC310 No Yes 0.11% 11689 100 152 166 164 CGCRC310 No Yes 0.15% 10273 100 153 166 164 CGCRC311 Yes No 0.86% 8456 94 160 171 172 CGCRC312 No Yes 0.59% 4719 100 160 165 173 CGCRC312 Yes Yes 0.47% 3391 101 157 172 170 CGCRC313 Yes Yes 0.17% 5013 100 163 166 174 CGCRC313 Yes Yes 0.07% 8150 72 161 171 174 CGCRC314 Yes Yes 0.30% 4684 100 158 165 169 CGCRC314 No Yes 2.50% 6902 85 159 165 170 CGCRC314 Yes Yes 0.38% 7229 102 158 167 170 CGCRC315 Yes Yes 0.27% 8733 94 155 167 169 CGCRC315 Yes Yes 0.25% 9623 101 158 166 170 CGCRC316 Yes Yes 6.52% 12880 100 150 166 163 CGCRC316 No Yes 5.74% 7479 93 157 164 168 CGCRC316 Yes Yes 5.47% 13682 100 149 165 162 CGCRC316 No No 0.11% 16716 85 153 166 156 CGCRC316 No Yes 0.13% 17060 100 150 166 153 CGCRC317 Yes Yes 0.36% 14587 84 152 166 154 CGCRC317 No Yes 0.23% 10483 100 152 164 155 CGCRC317 Yes No 0.29% 3497 101 149 166 153 CGCRC318 No Yes 0.25% 16436 98 158 170 170 CGCRC320 NA Yes 34.76% 6521 100 163 170 175 CGCRC320 No No 0.12% 11633 100 162 174 174 CGCRC321 No No 0.20% 6916 88 161 167 174 CGCRC321 Yes No 0.08% 9559 94 159 171 170 CGCRC321 NA Yes 41.86% 5545 100 159 172 172 CGCRC332 No Yes 19.98% 605 104 154 170 176 CGCRC333 No Yes 43.03% 1265 89 159 165 171 CGCRC333 Yes Yes 22.26% 3338 102 153 165 169 CGCRC333 No No 1.00% 3008 102 153 169 169 CGCRC334 Yes Yes 13.44% 1725 105 160 170 175 CGCRC334 NA Yes 35.28% 1168 100 159 164 174 CGCRC334 No No 3.85% 1798 103 159 166 173 CGCRC335 Yes Yes 0.32% 2411 99 155 167 157 CGCRC336 Yes Yes 75.26% 757 104 156 171 170 CGCRC336 Yes Yes 42.87% 1080 102 150 166 167 CGCRC336 No Yes 81.61% 391 102 161 165 171 CGCRC337 No No 0.12%, 6497 72 153 169 177 CGCRC337 NA Yes 46.26% 1686 100 147 170 163 CGCRC338 Yes Yes 27.03% 1408 105 153 164 166 CGCRC339 Yes Yes 1.94% 1256 105 158 168 169 CGCRC339 Yes Yes 2.35% 1639 101 158 165 172 CGCRC339 No Yes 3.14% 1143 100 154 170 167 CGCRC339 Yes Yes 1.71% 1584 108 161 171 173 CGCRC340 Yes Yes 18.26% 876 101 162 170 175 CGCRC340 Yes Yes 22.57% 796 105 159 164 174 CGPLBR38 No Yes 0.53% 9684 95 156 166 168 CGPLBR40 NA Yes 28.99% 10277 78 162 168 173 CGPLBR44 Yes Yes 1.82% 10715 99 162 171 173 CGPLBR44 No Yes 0.41% 10837 100 159 169 171 CGPLBR44 No Yes 0.13% 12640 100 159 168 171 CGPLBR48 NA Yes 34.61% 5631 100 164 170 179 CGPLBR48 No No 0.18% 12467 101 167 174 180 CGPLBR55 No No 0.18% 10527 101 158 169 169 CGPLBR55 Yes Yes 0.68% 6011 101 153 166 167 CGPLBR55 Yes Yes 0.42% 3973 101 153 166 166 CGPLBR63 NA Yes 34.82% 3405 97 165 170 176 CGPLBR67 Yes Yes 0.11% 10259 87 157 168 168 CGPLBR67 Yes Yes 0.68% 5163 100 151 167 166 CGPLBR67 No No 0.28% 6250 100 155 166 167 CGPLBR69 No No 0.29% 7558 100 159 166 170 CGPLBR69 NA Yes 41.74% 3938 101 154 169 166 CGPLBR69 NA Yes 41.66% 2387 101 157 166 168 CGPLBR70 No No 0.36% 6916 100 158 171 169 CGRLBR70 NA Yes 40.28% 3580 107 160 169 173 CGPLBR71 Yes Yes 0.10% 7930 85 156 166 158 CGPLBR72 NA Yes 44.03% 2389 100 157 160 170 CGPLBR73 No No 0.27% 11348 95 161 173 174 CGPLBR73 NA Yes 35.58% 3422 102 157 168 169 CGPLBR74 NA Yes 36.23% 3784 101 163 175 174 CGPLBR75 Yes Yes 0.14% 7290 103 162 173 172 CGPLBR76 NA Yes 36.57% 4342 104 166 171 179 CGPLBR76 Yes Yes 0.12% 11785 100 165 168 177 CGPLBR77 No Yes 2.29% 6161 100 158 166 169 CGPLBR80 No No 0.54% 3643 96 165 166 185 CGPLBR83 NA Yes 42.66% 3479 105 162 164 174 GGPLBR83 No No 0.28% 3496 103 165 170 177 CGPLBR83 NA Yes 44.91% 1748 100 164 173 175 CGPLBR86 NA Yes 42.32% 4241 98 160 168 175 CGPLBR86 NA Yes 43.38% 3096 88 160 167 174 CGPLBR87 No No 0.35% 3680 101 162 168 175 CGPLBR87 Yes No 0.31% 6180 101 163 164 175 CGPLBR87 No No 0.40% 7746 86 160 167 175 CGPLBR87 NA Yes 42.94% 2266 106 160 166 172 CGPLBR88 No No 0.13% 17537 89 185 200 223 CGPLBR88 NA Yes 31.19% 5919 101 162 172 173 CGPLBR92 No Yes 0.20% 15530 77 150 164 152 CGPLBR96 Yes No 0.10% 9893 100 159 164 171 CGPLBR96 No Yes 5.81% 8620 95 162 167 173 CGPLBR96 No No 0.60% 8036 85 162 169 175 CGPLBR97 Yes Yes 0.11% 14856 93 160 168 170 CGPLBR97 NA Yes 34.12% 5329 100 161 165 171 CGPLBR97 Yes Yes 0.13% 7010 97 158 169 170 CGPLLU144 Yes Yes 1.95% 11371 100 156 165 167 CGPLLU144 Yes Yes 5.10% 7641 100 155 167 166 CGPLLU144 No Yes 0.16% 9996 100 158 168 169 CGPLLU144 No No 0.22% 4956 101 159 166 169 CGPLLU144 Yes Yes 2.94% 8540 100 153 170 166 CGPLLU144 No No 0.18% 7648 101 156 164 166 CGPLLU146 Yes No 0.25% 5920 100 155 164 168 CGPLLU146 No Yes 1.30% 9356 100 155 166 168 CGPLLU146 No Yes 0.84% 7284 101 158 165 170 CGPLLU146 No Yes 0.87% 4183 103 160 166 170 CGPLLU146 No No 0.20% 6778 100 157 166 168 CGPLLU147 Yes No 0.15% 4807 100 155 166 170 CGPLLU147 No Yes 0.55% 5282 100 156 167 171 CGPLLU147 No Yes 0.94% 7122 100 158 174 173 CGPLLU147 NA Yes 43.47% 2825 101 160 165 173 CGPLLU162 No No 0.22% 9940 95 161 164 174 CGPLLU162 Yes Yes 0.22% 13855 87 160 174 173 CGPLLU162 No No 0.14% 11251 100 153 167 166 CGPLLU163 No No 0.21% 10805 85 159 165 173 CGPLLU163 No Yes 0.15% 20185 83 158 166 170 CGPLLU164 No Yes 1.23% 6795 91 156 161 169 CGPLLU164 NA Yes 42.52% 4561 92 157 164 169 CGPLLU164 No No 0.20% 8097 100 158 170 170 CGPLLU164 Yes No 0.10% 9241 100 155 165 157 CGPLLU164 No Yes 1.78% 10806 100 157 168 159 CGPLLU164 No Yes 1.86% 10919 100 157 168 159 CGPLLU164 No Yes 0.96% 5412 103 159 175 171 CGPLLU164 No No 0.22% 5151 101 160 166 169 CGPLLU165 NA Yes 36.62% 7448 95 155 167 167 CGPLLU165 Yes Yes 0.16% 5822 102 154 166 166 CGPLLU168 Yes Yes 0.06% 15985 97 152 165 166 CGPLLU168 No No 0.39% 11070 100 156 165 168 CGPLLU168 Yes Yes 0.07% 11063 83 157 166 169 CGPLLU174 No Yes 0.33% 5881 88 162 165 174 CGPLLU174 No Yes 0.40% 3696 100 162 167 172 CGPLLU174 Yes Yes 0.16% 4941 101 162 167 172 CGPLLU174 No Yes 0.29% 7527 100 163 168 173 CGPLLU174 No Yes 0.26% 8353 101 162 168 173 CGPLLU175 Yes Yes 8.03% 10214 100 160 166 170 CGPLLU175 No No 0.21% 9739 100 157 168 168 CGPLLU175 No Yes 0.15% 9509 100 157 165 168 CGPLLU175 NA Yes 43.84% 2710 101 157 165 167 CGPLLU175 No Yes 3.64% 6565 100 158 166 168 CGPLLU176 No Yes 0.92% 6513 101 164 168 175 CGPLLU176 No Yes 0.21% 5962 100 164 174 175 CGPLLU177 Yes Yes 2.49% 7044 102 160 165 170 CGPLLU177 No Yes 1.53% 9950 88 160 169 171 CGPLLU177 Yes No 0.29% 11233 100 160 168 171 CGPLLU177 No No 0.13% 10966 75 160 169 172 CGPLLU178 No No 0.29% 8378 100 162 176 172 CGPLLU178 No No 0.17% 7235 101 159 167 170 CGPLLU179 No Yes 0.38% 8350 103 161 169 171 CGPLLU179 NA Yes 39.91% 2609 103 162 171 173 CGPLLU180 No Yes 2.43% 6085 91 158 165 170 CGPLLU180 No Yes 2.07% 6680 92 158 164 169 CGPLLU180 No Yes 1.94% 7790 92 158 167 168 CGPLLU180 Yes No 0.08% 9036 101 160 169 171 CGPLLU180 No Yes 1.01% 4679 100 157 169 168 CGPLLU197 Yes No 0.16% 7196 102 162 166 172 CGPLLU197 No No 0.38% 7147 100 161 166 172 CGPLLU198 No Yes 0.87% 9322 97 157 165 168 CGPLLU198 Yes Yes 0.52% 8303 100 160 173 172 CGPLLU202 Yes Yes 0.05% 14197 90 151 165 166 CGPLLU202 No No 0.13% 9279 51 150 168 167 CGPLLU204 No No 0.26% 7185 100 157 165 168 CGPLLU205 No Yes 0.70% 10739 96 156 165 166 CGPLLU205 No Yes 3.47% 12065 100 154 165 165 CGPLLU206 Yes Yes 26.13% 6746 94 148 165 164 CGPLLU206 No No 0.21% 11225 100 147 167 164 CGPLLU207 Yes Yes 0.32% 11224 100 159 165 170 CGPLLU207 NA Yes 34.58% 4960 101 160 166 170 CGPLLU207 Yes No 0.09% 13216 85 161 165 172 CGPLLU208 Yes Yes 1.33% 5211 101 156 166 168 CGPLLU208 NA Yes 39.34% 5253 100 159 164 170 CGPLLU208 Yes Yes 0.86% 10233 100 160 170 171 CGPLLU208 No No 0.17% 11421 100 158 165 171 CGPLLU209 NA Yes 26.84% 11695 96 153 166 169 CGPLLU209 No Yes 9.97% 12771 94 155 163 168 CGPLLU209 Yes Yes 9.13% 16557 92 157 169 170 CGPLLU209 No Yes 9.32% 13057 97 158 167 171 CGPLLU209 NA Yes 30.41% 8521 100 155 167 169 CGPLOV10 Yes Yes 3.14% 4421 101 161 165 172 CGPLOV11 Yes Yes 0.87% 7987 100 157 164 169 CGPLOV11 NA Yes 37.77% 3782 97 160 166 171 CGPLOV13 No Yes 0.12% 12072 88 157 165 169 CGPLOV13 NA Yes 37.98% 4107 103 159 166 169 CGPLOV13 No Yes 0.35% 8427 100 161 165 171 CGPLOV14 No No 0.14% 11418 92 154 167 171 CGPLOV15 Yes Yes 3.54% 7689 102 157 164 169 CGPLOV15 No No 0.19% 7617 101 159 167 171 CGPLOV17 Yes Yes 0.32% 4463 96 155 168 163 CGPLOV17 NA Yes 44.10% 2884 110 157 170 170 CGPLOV18 NA Yes 40.81% 2945 101 159 164 169 CGPLOV19 NA Yes 23.80% 9727 95 158 167 172 CGPLOV19 Yes Yes 36.83% 4387 100 158 165 169 CGPLOV19 NA Yes 65.29% 2775 93 161 171 171 CGPLOV19 Yes Yes 46.35% 3818 102 156 170 170 CGPLOV20 Yes Yes 0.21% 5404 94 159 165 170 CGPLOV20 NA Yes 44.05% 3744 102 158 166 169 CGPLOV21 NA Yes 7.68% 21823 81 158 166 169 CGPLOV21 No Yes 2.04% 18806 101 159 165 169 CGPLOV21 No No 14.36% 10801 89 160 166 169 CGPLOV22 No Yes 0.49% 11952 100 155 165 167 CGPLOV22 Yes Yes 0.34% 12399 92 150 165 164 Wild-type Fragments Mutant Fragments 75th 25th Mean Percentile Maximum Minimum Percentile Mode Median cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Distinct Fragment Fragment Fragment Fragment Size (bp) Size (bp) Size (bp) Coverage Size (bp) Size (bp) Size (bp) Size (bp) 179 188 400 19 100 142 233 165 182 185 400 21 132 166 182 176 180 183 400 5411 92 152 167 169 177 182 400 1903 100 148 166 166 184 185 400 1344 108 155 167 170 181 182 400 2108 100 153 166 168 176 180 400 1951 101 149 175 167 176 183 399 75 123 162 167 172 177 182 400 23 101 130 130 139 183 188 399 6863 100 160 168 173 188 186 400 34 77 154 171 170 175 179 396 9 138 147 176 171 184 185 400 21 115 145 155 159 179 185 397 30 137 149 181 162 179 182 397 44 125 155 155 169 185 188 400 8167 101 160 166 171 187 188 400 3562 102 158 168 170 184 187 399 15 93 137 127 174 183 185 400 26 137 163 166 167 181 182 397 35 118 147 176 163 172 175 400 71 133 152 170 165 169 174 400 55 130 153 165 164 189 187 399 17 149 155 326 170 176 183 400 18 156 170 174 174 169 175 397 51 108 143 268 152 166 173 397 26 118 147 153 156 184 186 400 45 116 151 168 163 185 186 400 25 157 165 191 175 185 187 400 25 124 168 180 180 167 175 394 86 121 155 169 166 167 173 397 45 124 143 197 162 170 175 398 108 126 147 162 162 190 189 400 23 131 148 145 166 182 182 399 42 138 155 155 174 189 187 399 25 126 153 176 176 192 193 400 977 101 149 189 170 173 179 391 525 102 140 168 159 181 185 399 4010 100 158 166 170 178 184 399 625 100 140 167 162 175 179 398 37 111 143 142 166 181 186 396 3184 102 159 168 172 180 183 399 47 111 148 144 169 133 184 397 39 111 146 182 162 185 184 400 24 110 146 309 182 176 180 400 32 117 146 154 157 180 184 399 43 111 143 144 177 185 187 400 29 109 140 204 159 179 182 399 20 128 152 180 163 176 184 398 7515 101 160 170 171 182 182 399 31 85 146 137 166 181 182 395 428 100 135 138 149 175 180 397 352 97 136 132 147 165 172 397 15 131 137 132 144 170 173 398 25 107 138 159 161 171 173 400 27 122 147 161 161 189 189 400 91 112 165 168 173 189 189 400 27 124 144 154 154 178 184 399 24 105 143 132 159 188 189 399 8 122 143 122 161 194 192 400 17 144 163 173 173 180 183 394 15 132 159 186 166 183 185 399 233 131 162 167 172 186 186 398 27 136 155 183 163 192 195 399 23 137 144 175 152 182 184 399 29 131 157 177 171 166 172 396 1616 100 146 164 159 175 180 400 806 96 158 169 169 165 172 399 1410 102 140 149 154 170 177 397 49 99 153 143 182 166 173 398 33 140 155 154 170 180 178 400 73 95 140 140 155 172 177 400 38 115 160 164 167 171 174 386 6 124 137 170 156 180 183 400 70 124 151 151 164 194 199 399 6586 96 162 168 175 184 188 400 41 112 172 176 177 194 198 399 35 146 168 175 175 182 184 399 20 166 180 185 191 183 186 397 5338 102 159 175 171 202 203 393 178 101 150 168 171 195 195 397 1350 104 153 163 171 185 189 400 1257 100 153 168 170 185 189 396 30 117 163 164 172 203 210 391 336 105 153 141 171 188 194 399 741 101 161 169 176 193 193 396 89 100 145 171 171 172 179 396 12 129 143 143 153 186 188 387 3559 91 155 164 173 177 183 392 873 102 149 163 164 194 200 377 1909 100 158 167 176 202 259 400 27 122 157 164 179 171 178 395 1818 103 147 169 162 178 182 374 546 102 151 166 166 179 184 397 26 132 142 138 171 195 194 400 53 117 157 166 169 176 179 397 40 124 150 169 166 188 191 390 38 107 153 180 174 205 207 399 217 102 146 144 163 196 195 397 266 111 147 150 166 186 184 400 76 123 157 171 169 179 186 400 9832 93 161 166 172 191 190 400 277 104 162 160 176 191 189 400 65 123 165 166 172 187 189 400 31 136 163 171 167 202 202 400 5286 102 166 168 181 196 201 400 102 138 166 161 179 181 182 397 30 138 158 189 185 181 181 400 64 113 158 163 167 176 179 398 27 121 163 200 171 191 192 398 2943 100 165 176 176 179 181 399 25 138 153 138 167 171 177 399 60 110 136 147 147 172 179 399 26 139 147 180 176 186 184 398 35 121 149 360 161 176 178 397 4000 103 155 166 167 176 178 385 2390 99 157 164 168 182 184 400 28 131 160 168 167 194 193 400 3545 100 161 169 173 179 180 398 15 121 146 166 166 188 187 400 2587 103 158 162 169 189 192 400 86 121 165 183 177 178 184 399 3339 101 157 165 169 179 187 391 3193 101 163 178 173 183 186 398 13 111 153 153 161 197 201 400 4140 102 166 169 179 191 194 400 16 130 143 143 157 183 183 400 209 125 154 175 170 211 230 400 41 158 176 197 186 193 193 400 3445 94 162 175 174 197 199 400 23 123 182 248 224 193 195 399 1787 100 163 163 176 204 207 400 4100 100 159 164 173 196 195 400 3096 79 159 161 173 202 203 400 73 142 178 178 184 205 203 400 23 161 168 168 171 195 196 400 170 125 158 173 173 195 192 400 2086 101 162 169 176 238 280 400 125 84 192 194 207 197 194 400 5715 108 163 154 174 172 173 398 109 78 148 149 158 196 191 399 35 119 161 172 171 189 190 400 826 102 162 166 171 194 195 400 95 135 160 161 170 184 184 400 27 128 150 150 169 179 184 399 4771 103 161 168 171 187 185 399 7 147 154 154 167 179 179 395 330 106 152 165 166 172 177 399 536 106 151 167 163 179 183 400 45 138 163 175 172 182 182 397 16 138 146 146 155 172 177 397 293 101 152 169 164 171 177 399 23 130 152 162 162 180 183 399 54 104 161 154 176 184 184 400 154 96 149 157 163 186 187 399 79 102 163 177 174 183 185 400 44 118 149 163 163 182 184 400 35 136 164 204 181 192 191 400 13 138 164 169 169 199 205 400 50 128 155 161 171 191 193 400 81 108 150 108 173 190 191 389 2597 101 159 165 172 192 197 400 58 92 173 192 192 183 189 400 74 90 147 142 167 175 178 400 37 144 163 185 172 194 202 400 61 93 164 181 181 184 186 400 66 104 158 194 174 191 190 396 101 126 155 176 176 188 185 394 4718 100 156 164 168 186 186 399 30 134 161 175 175 180 180 397 34 139 163 155 170 182 182 400 262 101 150 152 165 182 182 400 277 101 150 147 166 180 182 395 65 121 158 161 167 177 182 400 16 144 172 179 179 185 184 399 7186 100 154 167 166 181 179 394 21 108 164 164 173 177 180 400 18 111 127 127 158 179 181 400 72 121 156 173 166 177 182 400 30 106 160 174 174 200 199 399 36 131 147 143 177 184 185 392 20 144 173 266 178 182 184 395 16 147 156 156 164 186 187 399 34 159 168 168 176 186 186 396 5 116 182 182 185 185 183 399 1073 100 142 164 152 179 180 400 46 109 151 143 175 181 181 400 30 146 154 146 168 176 179 392 2742 102 154 164 166 174 180 399 298 103 140 148 150 197 194 399 67 115 164 250 173 195 194 399 19 156 165 165 185 178 182 395 189 105 138 141 150 183 185 398 227 123 160 168 169 185 184 397 53 78 161 175 175 190 188 395 50 130 161 168 168 186 187 396 28 139 150 173 170 179 184 400 24 130 153 176 170 185 185 394 48 111 154 170 168 189 187 398 2337 100 163 166 172 198 200 396 172 83 152 160 166 190 188 400 215 123 151 159 163 184 184 400 207 121 151 157 161 191 189 397 17 143 170 217 214 181 182 398 52 122 152 167 164 191 189 399 17 109 161 173 171 191 189 399 40 136 164 166 171 180 181 399 127 88 149 131 162 181 186 400 68 141 166 175 176 169 179 398 10 81 167 167 167 170 181 398 33 107 162 167 167 175 181 391 23 112 156 190 164 175 177 400 109 130 153 169 166 172 176 400 684 105 153 167 166 179 178 398 2946 100 138 157 155 175 178 399 30 121 165 165 176 187 186 400 63 140 155 154 167 181 184 400 4754 101 160 170 170 182 187 400 31 131 162 162 174 181 183 400 150 110 144 166 162 179 184 400 5290 95 159 167 169 181 186 400 140 101 155 175 167 187 190 397 20 92 141 241 168 190 192 400 8065 85 156 164 169 174 182 400 2586 101 147 165 165 185 188 400 2808 100 150 158 167 182 187 400 2227 100 154 162 171 176 183 396 8425 100 155 165 169 186 188 399 142 112 146 140 159 186 185 399 104 132 158 159 167 183 185 392 3462 101 160 173 172 182 183 399 25 94 140 140 158 177 181 399 3789 101 159 168 169 181 184 400 57 131 152 170 170 183 191 400 36 118 154 201 182 187 185 399 362 110 152 143 180 182 188 400 20 158 153 311 174 186 187 397 23 126 151 184 168 188 189 400 2980 100 158 169 170 183 183 391 2793 91 158 167 170 185 189 395 7357 100 158 175 171 184 184 398 5186 101 157 165 170 182 187 400 15595 64 159 167 170 186 185 400 6749 101 158 167 170 193 190 400 23 127 148 148 194 182 185 394 3901 101 160 167 171 179 180 400 4633 100 158 169 170 175 179 400 734 101 151 155 165 175 180 394 4022 101 159 167 168 184 182 400 117 116 156 156 172 172 176 395 65 109 145 177 167 Adjusted Difference Difference P Value of between between Difference Wild-type Mutant Fragments Median Mean between Fragments 75th Mutant and Mutant and Mutant and Mean Mean Percentile Maximum Wild-type Wild-Type Wild-type cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Fragment Fragment Fragment Size (bp) Size (bp) Size (bp) Size (bp) Sizes (bp) Sizes (bp) Sizes 179 180 230 305 −4.0 1.54 0.475 182 191 198 309 7.0 8.33 0.250 180 186 191 399 0.0 5.89 0.000 177 177 183 383 −1.0 −0.25 0.874 184 189 131 398 1.0 5.37 0.009 181 165 187 386 1.0 3.30 0.025 176 179 182 397 0.0 2.65 0.148 176 182 190 370 3.0 5.31 0.368 177 164 155 345 −29.5 −12.73 0.000 183 186 189 400 2.0 3.13 0.002 188 177 192 335 −0.5 −11.46 0.571 175 177 176 290 4.0 1.22 0.475 184 176 175 368 −11.0 −7.99 0.052 179 182 181 369 −8.0 3.49 0.061 179 185 194 338 0.0 5.78 0.023 185 184 187 400 −1.0 −1.27 0.212 187 185 185 399 0.0 −2.62 0.114 184 173 193 261 3.0 −11.00 0.507 183 179 180 364 −3.0 −4.34 0.430 181 172 176 336 −6.0 −9.35 0.166 172 169 173 301 0.0 3.57 0.668 169 166 168 325 0.0 −2.15 0.630 189 221 301 387 −3.0 32.43 0.453 176 210 219 372 5.0 33.84 0.368 169 164 178 268 −12.0 −5.12 0.000 166 174 158 327 −9.5 8.37 0.036 184 175 177 346 −8.0 −8.84 0.057 185 207 199 350 3.0 22.93 0.465 185 189 191 338 8.0 4.06 0.154 167 168 175 309 2.0 0.46 0.445 167 166 168 377 −1.0 −0.91 0.482 170 164 174 302 −3.0 6.74 0.064 190 189 205 333 −5.0 −0.80 0.297 182 177 187 343 5.5 −4.51 0.171 189 188 229 305 7.0 −0.19 0.234 192 182 192 380 −1.0 −9.76 0.000 173 168 176 382 −7.0 −5.57 0.052 181 181 185 398 0.0 0.37 0.773 178 172 181 380 −9.0 −6.68 0.009 175 172 186 321 −1.0 2.38 0.572 181 182 187 400 0.5 0.95 0.564 180 176 183 353 −1.0 −4.83 0.598 133 182 185 337 −7.0 −0.44 0.064 185 208 284 355 14.0 22.31 0.031 176 167 166 298 −11.0 −8.94 0.013 180 187 212 319 9.0 7.22 0.062 185 188 204 387 −12.0 3.32 0.031 179 166 180 219 −6.5 −13.04 0.155 176 177 185 400 1.0 1.08 0.166 182 167 176 316 −3.0 −14.62 0.469 181 158 166 340 −20.0 −23.47 0.000 175 149 159 326 21.0 26.04 0.000 165 163 171 323 −20.0 −1.73 0.000 170 175 190 299 −3.0 4.33 0.384 171 173 171 342 −3.0 2.54 0.354 189 196 192 379 1.0 6.83 0.571 189 167 172 320 −19.0 22.39 0.000 178 183 190 367 −11.0 4.97 0.054 188 168 195 241 −13.0 −19.21 0.100 194 213 261 372 1.0 19.22 0.587 180 174 185 265 −3.0 −5.62 0.461 183 190 187 394 2.0 7.27 0.137 186 170 178 262 −7.0 −16.03 0.131 192 190 212 327 −17.0 −1.76 0.018 182 183 179 319 1.0 0.74 0.564 166 163 170 354 −3.5 −3.57 0.000 175 179 184 366 1.0 3.80 0.054 165 164 170 398 −8.0 −0.35 0.816 170 206 284 333 16.0 36.25 0.000 166 180 180 296 7.0 14.38 0.104 180 173 178 324 −9.0 −6.66 0.000 172 182 179 329 1.5 10.09 0.479 171 153 168 178 −7.5 18.98 0.411 180 182 183 385 −6.0 1.71 0.064 194 193 196 399 0.0 −1.79 0.166 184 195 195 373 3.0 11.02 0.397 194 181 186 312 1.0 −13.40 0.587 182 205 219 357 21.0 23.48 0.013 183 183 185 394 −1.0 0.03 0.984 202 198 240 357 −5.0 −4.34 0.571 195 201 258 400 0.0 5.94 0.066 185 189 202 392 1.0 4.37 0.064 185 175 179 372 3.0 −10.29 0.463 203 200 240 399 4.0 3.10 0.571 188 190 194 400 2.0 1.96 0.571 193 197 229 393 −2.0 3.42 0.479 172 163 166 275 −14.0 −8.99 0.084 186 195 211 398 3.0 8.92 0.001 177 177 181 400 −3.0 −0.39 0.880 194 202 242 398 5.0 7.98 0.061 202 199 231 350 2.0 −3.82 0.685 171 173 180 396 1.0 1.92 0.372 178 180 182 381 0.0 2.87 0.416 179 183 188 351 1.5 3.29 0.572 195 192 198 336 −3.0 −2.86 0.451 176 181 176 309 −1.0 4.53 0.539 188 185 210 326 0.5 −2.59 0.576 205 188 212 360 −12.0 −17.11 0.004 196 188 204 379 −8.0 −7.53 0.208 186 182 182 346 1.0 −3.64 0.479 179 180 186 399 −1.0 1.04 0.155 191 201 200 384 3.0 9.95 0.061 191 198 192 371 1.0 7.08 0.560 187 201 199 387 −4.0 14.14 0.341 202 201 203 400 2.0 −0.88 0.587 196 199 209 372 −1.5 2.90 0.679 181 191 191 311 16.0 9.25 0.000 181 179 176 318 0.0 −2.85 0.679 176 187 190 392 5.0 10.89 0.314 191 187 192 398 0.0 −3.83 0.015 179 181 184 340 −1.0 2.00 0.571 171 161 159 327 −19.0 −9.77 0.000 172 176 184 344 9.0 3.52 0.015 186 197 195 360 −9.0 10.77 0.314 176 176 178 397 0.5 0.65 0.610 176 178 180 400 0.0 1.78 0.314 182 177 179 338 −2.0 −5.83 0.463 194 194 192 399 0.0 0.40 0.825 179 172 204 221 −2.0 −7.32 0.564 188 189 186 399 −1.0 1.12 0.598 189 189 193 373 3.0 −0.01 0.293 178 177 184 400 0.0 −1.73 0.598 179 180 186 389 1.0 0.22 0.839 183 171 179 323 −11.0 −12.36 0.061 197 197 200 400 0.0 −0.32 0.839 191 173 173 325 −20.0 −18.40 0.000 183 196 233 357 1.0 12.55 0.025 211 215 220 374 1.0 3.72 0.603 193 194 194 399 0.0 0.65 0.714 197 232 260 359 47.0 34.97 0.000 193 192 194 400 1.0 −0.85 0.718 204 200 202 400 −2.0 −3.65 0.062 196 194 191 397 −1.0 −2.45 0.251 202 237 338 377 9.0 35.30 0.114 205 189 186 380 −4.0 −16.38 0.435 195 188 190 400 −2.0 −6.17 0.293 195 203 203 400 4.5 8.80 0.000 238 243 324 400 −16.0 5.51 0.574 197 200 196 400 1.0 2.87 0.065 172 166 173 302 −4.0 −5.94 0.190 196 191 180 390 0.0 −4.34 0.627 189 187 187 395 −2.0 −1.94 0.475 194 182 184 400 −5.0 −11.54 0.155 184 174 185 319 −1.0 −9.68 0.571 179 179 183 400 0.0 0.15 0.880 187 164 174 177 −3.0 −22.90 0.155 179 178 178 361 −1.0 −1.35 0.685 172 172 175 363 −3.0 −0.34 0.880 179 185 191 380 3.0 6.52 0.368 182 162 170 224 −14.0 −19.82 0.007 172 170 174 392 2.0 1.37 0.646 171 163 177 232 −4.0 −7.62 0.252 180 195 206 383 7.5 14.58 0.064 184 176 185 347 −5.5 −7.87 0.154 186 200 203 372 4.0 14.61 0.270 183 185 188 338 −7.0 1.98 0.039 182 194 203 369 13.0 11.80 0.039 192 198 173 333 −1.0 6.05 0.610 199 216 301 360 0.0 17.02 0.623 191 198 224 385 0.0 6.48 0.624 190 185 187 397 −1.0 −5.17 0.005 192 202 200 397 18.0 9.79 0.007 183 176 182 391 −6.5 −6.78 0.061 175 192 186 375 6.0 17.15 0.005 194 197 211 370 8.0 3.34 0.169 184 189 194 379 3.5 4.60 0.270 191 194 213 331 7.0 2.50 0.718 188 190 187 393 −1.0 2.54 0.113 186 190 208 339 5.0 4.07 0.302 180 178 175 349 3.0 −1.65 0.407 182 181 186 393 −4.0 −0.65 0.876 182 182 185 393 −3.0 0.36 0.926 180 186 188 338 −4.0 6.15 0.234 177 187 180 376 10.0 9.98 0.130 185 183 181 396 −1.0 −1.73 0.154 181 196 200 357 7.0 14.95 0.213 177 189 186 352 −8.0 12.47 0.179 179 183 179 396 −2.0 4.31 0.427 177 180 186 282 5.0 3.09 0.252 200 196 227 298 2.5 −4.24 0.479 184 199 215 269 6.0 15.13 0.252 182 177 169 302 8.0 4.82 0.119 186 206 196 365 3.0 20.55 0.415 186 201 192 329 12.0 14.52 0.263 185 157 164 346 −18.0 −27.67 0.000 179 174 183 325 7.0 −5.22 0.054 181 186 181 367 −0.5 5.19 0.568 176 176 178 387 −1.0 −0.24 0.874 174 152 162 288 −18.0 −22.25 0.000 197 187 201 366 2.0 9.89 0.425 195 197 199 361 10.0 2.20 0.154 178 164 175 348 −20.0 −14.58 0.000 183 185 184 396 −2.0 1.68 0.706 185 189 188 392 4.0 3.80 0.241 190 184 175 377 −4.5 −5.86 0.234 186 170 173 354 2.5 −15.88 0.416 179 193 199 359 0.0 13.13 0.598 185 173 183 295 −3.0 −11.80 0.270 189 187 185 394 −1.0 −1.27 0.564 198 193 226 396 −4.0 −4.93 0.490 190 188 196 365 −6.0 −1.72 0.735 184 181 179 365 −7.0 −3.01 0.571 191 198 217 294 43.0 7.08 0.000 181 179 173 372 −4.5 −2.07 0.137 191 181 174 293 −1.0 −9.24 0.576 191 185 185 335 −1.0 −5.86 0.571 180 168 178 311 −6.0 −11.80 0.005 181 198 207 387 4.0 17.11 0.184 169 159 176 182 1.0 −10.20 0.589 170 174 185 322 0.0 4.57 0.636 175 175 190 349 −4.0 −0.92 0.308 175 175 178 382 0.0 −0.09 0.987 172 172 175 385 1.0 0.00 0.999 179 172 174 398 −9.0 −7.28 0.000 175 198 219 325 12.0 22.37 0.007 187 201 215 372 −3.0 13.70 0.286 181 179 181 393 0.0 −1.72 0.154 182 180 185 352 2.0 2.26 0.494 181 176 173 385 −6.0 −5.86 0.314 179 179 184 400 −1.0 0.11 0.909 181 179 180 352 −4.5 −2.77 0.589 187 178 209 283 −3.0 −9.82 0.479 190 190 190 399 0.0 −0.08 0.942 174 169 179 386 −3.5 −4.59 0.000 185 189 200 399 −3.0 4.17 0.007 182 183 190 398 0.0 1.00 0.564 176 176 184 400 0.0 0.54 0.568 186 180 193 352 −13.0 −5.41 0.463 186 189 180 331 −2.0 3.05 0.657 183 184 187 396 1.0 0.82 0.576 182 159 163 341 −11.0 −23.47 0.027 177 176 181 395 0.0 −0.66 0.576 181 179 184 327 −1.0 −2.41 0.568 183 187 201 328 11.0 3.60 0.114 187 207 268 389 11.0 20.70 0.000 182 198 209 311 3.0 15.25 0.475 185 185 185 328 −1.0 −1.49 0.571 188 187 189 398 0.0 −0.84 0.637 183 181 182 389 1.0 −2.30 0.171 185 182 187 399 −1.0 −2.37 0.008 184 185 186 400 1.0 1.72 0.240 182 181 185 397 −1.0 −1.39 0.245 186 185 187 400 0.0 −0.52 0.702 193 222 292 378 24.0 29.58 0.027 182 182 185 398 2.0 0.32 0.821 179 185 187 400 1.0 6.16 0.000 175 176 178 366 −4.0 0.48 0.823 175 172 178 399 −1.0 2.84 0.000 184 199 184 399 5.0 15.08 0.084 172 181 181 306 3.0 9.11 0.293

indicates data missing or illegible when filed

TABLE 4 APPENDIX - D: Summary of whole genome cfDNA analyses Analysis Patient Read Total Bases High Quality Patient Timepoint type Type Length Sequenced Bases Analyzed Coverage CGCRC291 Preoperative WGS Colorectal 100 7232125000 4695396600 1.86 treatment naïve Cancer CGCRC292 Preoperative WGS Colorectal 100 6794092800 4471065400 1.77 treatment naïve Cancer CGCRC293 Preoperative WGS Colorectal 100 8373899600 5686176000 2.26 treatment naïve Cancer CGCRC294 Preoperative WGS Colorectal 100 8081312000 5347045800 2.12 treatment naïve Cancer CGCRC296 Preoperative WGS Colorectal 100 10072029200 6770998200 2.69 treatment naïve Cancer CGCRC299 Preoperative WGS Colorectal 100 10971591600 7632723200 3.03 treatment naïve Cancer CGCRC300 Preoperative WGS Colorectal 100 9894332600 6699951000 2.66 treatment naïve Cancer CGCRC301 Preoperative WGS Colorectal 100 7857346200 5021002000 1.99 treatment naïve Cancer CGCRC302 Preoperative WGS Colorectal 100 11671913000 8335275800 3.31 treatment naïve Cancer CGCRC304 Preoperative WGS Colorectal 100 19011739200 12957614200 5.14 treatment naïve Cancer CGCRC305 Preoperative WGS Colorectal 100 7177341400 4809957200 1.91 treatment naïve Cancer CGCRC306 Preoperative WGS Colorectal 100 8302233200 5608043600 2.23 treatment naïve Cancer CGCRC307 Preoperative WGS Colorectal 100 8034729400 5342620000 2.12 treatment naïve Cancer CGCRC308 Preoperative WGS Colorectal 100 8670084800 5934037200 2.35 treatment naïve Cancer CGCRC311 Preoperative WGS Colorectal 100 6947634400 4704601800 1.87 treatment naïve Cancer CGCRC315 Preoperative WGS Colorectal 100 5205544000 3419565400 1.36 treatment naïve Cancer CGCRC316 Preoperative WGS Colorectal 100 6405388600 4447534800 1.76 treatment naïve Cancer CGCRC317 Preoperative WGS Colorectal 100 6060390400 4104616600 1.63 treatment naïve Cancer CGCRC318 Preoperative WGS Colorectal 100 6848768600 4439404800 1.76 treatment naïve Cancer CGCRC319 Preoperative WGS Colorectal 100 10545294400 7355181600 2.92 treatment naïve Cancer CGCRC320 Preoperative WGS Colorectal 100 5961999200 3945054000 1.57 treatment naïve Cancer CGCRC321 Preoperative WGS Colorectal 100 8248095400 5614355000 2.23 treatment naïve Cancer CGCRC333 Preoperative WGS Colorectal 100 10540267600 6915490600 2.74 treatment naïve Cancer CGCRC336 Preoperative WGS Colorectal 100 10675581800 7087691800 2.81 treatment naïve Cancer CGCRC338 Preoperative WGS Colorectal 100 13788172600 3970308600 3.56 treatment naïve Cancer CGCRC341 Preoperative WGS Colorectal 100 10753467600 7311539200 2.90 treatment naïve Cancer CGCRC342 Preoperative WGS Colorectal 100 11836966000 7552793200 3.00 treatment naïve Cancer CGH14 Human adult elutriated WGS Healthy 100 36525427600 24950300200 9.90 lymphocytes CGH15 Human adult elutriated WGS Healthy 100 29930855000 23754049400 9.43 lymphocytes CGLU316 Pre-treatment, Day −53 WGS Lung 100 10354123200 6896471400 2.74 Cancer CGLU316 Pre-treatment, Day −4 WGS Lung 100 7870039200 5254938800 2.09 Cancer CGLU316 Post-treatment, Day 18 WGS Lung 100 8155322000 5416262400 2.15 Cancer CGLU316 Post-treatment, Day 87 WGS Lung 100 9442310400 6087893400 2.42 Cancer CGLU344 Pre-treatment, Day −21 WGS Lung 100 8728318600 5769097200 2.29 Cancer CGLU344 Pre-treatment, Day 0 WGS Lung 100 11710249400 7826902600 3.11 Cancer CGLU344 Post-treatment, Day 0.1875 WGS Lung 100 11569683000 7654701600 3.04 Cancer CGLU344 Post-treatment, Day 59 WGS Lung 100 11042459200 6320138800 2.51 Cancer CGLU369 Pre-treatment, Day −2 WGS Lung 100 8630932800 5779595800 2.29 Cancer CGLU369 Post-treatment, Day 12 WGS Lung 100 9227709600 6136755200 2.44 Cancer CGLU369 Post-treatment, Day 68 WGS Lung 100 7995282600 5239077200 2.08 Cancer CGLU369 Post-treatment, Day 110 WGS Lung 100 8750541000 5626139000 2.23 Cancer CGLU373 Pre-treatment, Day −2 WGS Lung 100 11746059600 7547485800 3.00 Cancer CGLU373 Post-treatment, Day 0.125 WGS Lung 100 13801136800 9255579400 3.67 Cancer CGLU373 Post-treatment, Day 7 WGS Lung 100 11537896800 7654111200 3.04 Cancer CGLU373 Post-treatment, Day 47 WGS Lung 100 8046326400 5397702400 2.14 Cancer CGPLBR100 Preoperative WGS Breast 100 8440532400 5729474800 2.27 treatment naïve Cancer CGPLBR101 Preoperative WGS Breast 100 9786253600 6673495200 2.65 treatment naïve Cancer CGPLBR102 Preoperative WGS Breast 100 8664980400 5669781600 2.25 treatment naïve Cancer CGPLBR103 Preoperative WGS Breast 100 9846936200 6662883400 2.64 treatment naïve Cancer CGPLBR104 Preoperative WGS Breast 100 9443375400 6497061000 2.58 treatment naïve Cancer CGPLBR12 Preoperative WGS Breast 100 7017577800 4823327400 1.91 treatment naïve Cancer CGPLBR18 Preoperative WGS Breast 100 10309652800 7130386000 2.83 treatment naïve Cancer CGPLBR23 Preoperative WGS Breast 100 9034484800 6219625800 2.47 treatment naïve Cancer CGPLBR24 Preoperative WGS Breast 100 9891454200 6601857400 2.62 treatment naïve Cancer CGPLBR28 Preoperative WGS Breast 100 7997607200 5400803200 2.14 treatment naïve Cancer CGPLBR30 Preoperative WGS Breast 100 8502597200 5885822400 2.34 treatment naïve Cancer CGPLBR31 Preoperative WGS Breast 100 12660085600 8551995600 3.39 treatment naïve Cancer CGPLBR32 Preoperative WGS Breast 100 8773498600 5839034600 2.32 treatment naïve Cancer CGPLBR33 Preoperative WGS Breast 100 10931742800 6967030600 2.76 treatment naïve Cancer CGPLBR34 Preoperative WGS Breast 100 10861398600 7453225800 2.96 treatment naïve Cancer CGPLBR35 Preoperative WGS Breast 100 9180193600 6158440200 2.44 treatment naïve Cancer CGPLBR36 Preoperative WGS Breast 100 9159948400 6091817800 2.42 treatment naïve Cancer CGPLBR37 Preoperative WGS Breast 100 10307505800 6929530600 2.75 treatment naïve Cancer CGPLBR38 Preoperative WGS Breast 100 9983824000 6841725400 2.71 treatment naïve Cancer CGPLBR40 Preoperative WGS Breast 100 10148823800 7024345400 2.79 treatment naïve Cancer CGPLBR41 Preoperative WGS Breast 100 11168192000 7562945800 3.00 treatment naïve Cancer CGPLBR45 Preoperative WGS Breast 100 8793780600 6011109400 2.39 treatment naïve Cancer CGPLBR46 Preoperative WGS Breast 100 7228607600 4706130000 1.87 treatment naïve Cancer CGPLBR47 Preoperative WGS Breast 100 7906911400 5341655000 2.12 treatment naïve Cancer CGPLBR48 Preoperative WGS Breast 100 6992032000 4428636200 1.76 treatment naïve Cancer CGPLBR49 Preoperative WGS Breast 100 7311195000 4559460200 1.81 treatment naïve Cancer CGPLBR50 Preoperative WGS Breast 100 11107960600 7582776600 3.01 treatment naïve Cancer CGPLBR51 Preoperative WGS Breast 100 8393547400 5102069000 2.02 treatment naïve Cancer CGPLBR52 Preoperative WGS Breast 100 9491894800 6141729000 2.44 treatment naïve Cancer CGPLBR55 Preoperative WGS Breast 100 9380109800 6518855200 2.59 treatment naïve Cancer CGPLBR56 Preoperative WGS Breast 100 12191816800 8293011200 3.29 treatment naïve Cancer CGPLBR57 Preoperative WGS Breast 100 9847584400 6713638000 2.66 treatment naïve Cancer CGPLBR59 Preoperative WGS Breast 100 7476477000 5059878200 2.01 treatment naïve Cancer CGPLBR60 Preoperative WGS Breast 100 6531354600 4331253800 1.72 treatment naïve Cancer CGPLBR61 Preoperative WGS Breast 100 9311029200 6430920800 2.55 treatment naïve Cancer CGPLBR63 Preoperative WGS Breast 100 8971949000 6044009600 2.40 treatment naïve Cancer CGPLBR65 Preoperative WGS Breast 100 7197301400 4835015200 1.92 treatment naïve Cancer CGPLBR68 Preoperative WGS Breast 100 10003774000 6974918800 2.77 treatment naïve Cancer CGPLBR69 Preoperative WGS Breast 100 10080881800 6903459200 2.74 treatment naïve Cancer CGPLBR70 Preoperative WGS Breast 100 8824002800 6002533800 2.38 treatment naïve Cancer CGPLBR71 Preoperative WGS Breast 100 10164136800 6994668600 2.78 treatment naïve Cancer CGPLBR72 Preoperative WGS Breast 100 18416841400 12328783000 4.89 treatment naïve Cancer CGPLBR73 Preoperative WGS Breast 100 10281460200 7078613200 2.81 treatment naïve Cancer CGPLBR76 Preoperative WGS Breast 100 10105270400 6800705000 2.70 treatment naïve Cancer CGPLBR81 Preoperative WGS Breast 100 5087126000 3273367200 1.30 treatment naïve Cancer CGPLBR82 Preoperative WGS Breast 100 10576496600 7186662600 2.85 treatment naïve Cancer CGPLBR83 Preoperative WGS Breast 100 8977124400 5947525000 2.36 treatment naïve Cancer CGPLBR84 Preoperative WGS Breast 100 6272538600 4066870600 1.61 treatment naïve Cancer CGPLBR87 Preoperative WGS Breast 100 8460954800 5375710200 2.13 treatment naïve Cancer CGPLBR88 Preoperative WGS Breast 100 8665810400 5499898200 2.18 treatment naïve Cancer CGPLBR90 Preoperative WGS Breast 100 6663469200 4392442400 1.74 treatment naïve Cancer CGPLBR91 Preoperative WGS Breast 100 10933002400 7647842000 3.03 treatment naïve Cancer CGPLBR92 Preoperative WGS Breast 100 10392674000 6493598000 2.58 treatment naïve Cancer CGPLBR93 Preoperative WGS Breast 100 5659836000 3931106800 1.56 treatment naïve Cancer CGPLH189 Preoperative WGS Healthy 100 11400610400 7655568800 3.04 treatment naïve CGPLH190 Preoperative WGS Healthy 100 11444671600 7581175200 3.01 treatment naïve CGPLH192 Preoperative WGS Healthy 100 12199010800 8126804800 3.22 treatment naïve CGPLH193 Preoperative WGS Healthy 100 10201897600 6635285400 2.63 treatment naïve CGPLH194 Preoperative WGS Healthy 100 11005087400 7081652600 2.81 treatment naïve CGPLH196 Preoperative WGS Healthy 100 12891462800 8646881800 3.43 treatment naïve CGPLH197 Preoperative WGS Healthy 100 11961841600 8052855200 3.20 treatment naïve CGPLH198 Preoperative WGS Healthy 100 13605489000 8885716000 3.53 treatment naïve CGPLH199 Preoperative WGS Healthy 100 1818090200 5615316000 2.23 treatment naïve CGPLH200 Preoperative WGS Healthy 100 14400027600 9310342000 3.69 treatment naïve CGPLH201 Preoperative WGS Healthy 100 6208766800 4171848400 1.66 treatment naïve CGPLH202 Preoperative WGS Healthy 100 11282922800 7363530600 2.92 treatment naïve CGPLH203 Preoperative WGS Healthy 100 13540689600 9068747600 3.60 treatment naïve CGPLH205 Preoperative WGS Healthy 100 10343537800 6696988600 2.66 treatment naïve CGPLH208 Preoperative WGS Healthy 100 12796300000 8272073400 3.28 treatment naïve CGPLH209 Preoperative WGS Healthy 100 13123035400 8531813600 3.39 treatment naïve CGPLH210 Preoperative WGS Healthy 100 10184218800 6832204600 2.71 treatment naïve CGPLH211 Preoperative WGS Healthy 100 14655260200 8887067600 3.53 treatment naïve CGPLH300 Preoperative WGS Healthy 100 7062083400 4553351200 1.81 treatment naïve CGPLH307 Preoperative WGS Healthy 100 7239128200 4547697200 1.80 treatment naïve CGPLH308 Preoperative WGS Healthy 100 8512551400 5526653600 2.19 treatment naïve CGPLH309 Preoperative WGS Healthy 100 11664474200 7431836600 2.95 treatment naïve CGPLH310 Preoperative WGS Healthy 100 11045691000 7451506200 2.96 treatment naïve CGPLH311 Preoperative WGS Healthy 100 10406803200 6786479600 2.69 treatment naïve CGPLH314 Preoperative WGS Healthy 100 10371343800 6925866600 2.75 treatment naïve CGPLH315 Preoperative WGS Healthy 100 9508538400 6208744600 2.46 treatment naïve CGPLH316 Preoperative WGS Healthy 100 10131063600 6891181000 2.73 treatment naïve CGPLH317 Preoperative WGS Healthy 100 8364314400 5302232600 2.10 treatment naïve CGPLH319 Preoperative WGS Healthy 100 8780528200 5585897000 2.22 treatment naïve CGPLH320 Preoperative WGS Healthy 100 8956232600 5784619200 2.30 treatment naïve CGPLH322 Preoperative WGS Healthy 100 9563837800 6445517800 2.56 treatment naïve CGPLH324 Preoperative WGS Healthy 100 6765038600 4469201600 1.77 treatment naïve CGPLH325 Preoperative WGS Healthy 100 8008213400 5099262800 2.02 treatment naïve CGPLH326 Preoperative WGS Healthy 100 9554226200 6112544800 2.43 treatment naïve CGPLH327 Preoperative WGS Healthy 100 8239168800 5351280200 2.12 treatment naïve CGPLH328 Preoperative WGS Healthy 100 7197086800 4516894800 1.79 treatment naïve CGPLH329 Preoperative WGS Healthy 100 8921554800 5493709800 2.18 treatment naïve CGPLH330 Preoperative WGS Healthy 100 10693603400 7077793600 2.81 treatment naïve CGPLH331 Preoperative WGS Healthy 100 8982792000 5538096200 2.20 treatment naïve CGPLH333 Preoperative WGS Healthy 100 7856985400 5178829600 2.06 treatment naïve CGPLH335 Preoperative WGS Healthy 100 9370663400 6035739400 2.40 treatment naïve CGPLH336 Preoperative WGS Healthy 100 8002498200 5340331400 2.12 treatment naïve CGPLH337 Preoperative WGS Healthy 100 7399022000 4954467600 1.97 treatment naïve CGPLH338 Preoperative WGS Healthy 100 8917121600 6170927200 2.45 treatment naïve CGPLH339 Preoperative WGS Healthy 100 8591130800 5866411400 2.33 treatment naïve CGPLH340 Preoperative WGS Healthy 100 8046351000 5368062000 2.13 treatment naïve CGPLH341 Preoperative WGS Healthy 100 7914788600 5200304800 2.06 treatment naïve CGPLH342 Preoperative WGS Healthy 100 8633473000 5701972400 2.26 treatment naïve CGPLH343 Preoperative WGS Healthy 100 6694769800 4410670800 1.75 treatment naïve CGPLH344 Preoperative WGS Healthy 100 7628192400 4961476600 1.97 treatment naïve CGPLH345 Preoperative WGS Healthy 100 7121569400 4747223000 1.88 treatment naïve CGPLH346 Preoperative WGS Healthy 100 7707924600 4873321600 1.93 treatment naïve CGPLH35 Preoperative WGS Healthy 100 47305985200 4774186200 12.63 treatment naïve CGPLH350 Preoperative WGS Healthy 100 9745839800 6054055200 2.40 treatment naïve CGPLH351 Preoperative WGS Healthy 100 13317435800 6714465000 3.46 treatment naïve CGPLH352 Preoperative WGS Healthy 100 7659351600 4752309400 1.89 treatment naïve CGPLH353 Preoperative WGS Healthy 100 8435782400 5275098200 2.09 treatment naïve CGPLH354 Preoperative WGS Healthy 100 8018644000 4857577600 1.93 treatment naïve CGPLH355 Preoperative WGS Healthy 100 8624675800 5709726400 2.27 treatment naïve CGPLH356 Preoperative WGS Healthy 100 8817952800 5729595200 2.27 treatment naïve CGPLH357 Preoperative WGS Healthy 100 11931696200 7690004400 3.05 treatment naïve CGPLH358 Preoperative WGS Healthy 100 12802561200 8451274800 3.35 treatment naïve CGPLH36 Preoperative WGS Healthy 100 40173545600 3974810400 10.52 treatment naïve CGPLH360 Preoperative WGS Healthy 100 7280078400 4918566200 1.95 treatment naïve CGPLH361 Preoperative WGS Healthy 100 7493498400 4966813800 1.97 treatment naïve CGPLH362 Preoperative WGS Healthy 100 11345644200 7532133600 2.99 treatment naïve CGPLH363 Preoperative WGS Healthy 100 6117382800 3965952400 1.57 treatment naïve CGPLH364 Preoperative WGS Healthy 100 10823498400 7195657000 2.86 treatment naïve CGPLH365 Preoperative WGS Healthy 100 5938367400 3954556200 1.57 treatment naïve CGPLH366 Preoperative WGS Healthy 100 7063168600 4731853000 1.88 treatment naïve CGPLH367 Preoperative WGS Healthy 100 7119631800 4627888200 1.84 treatment naïve CGPLH368 Preoperative WGS Healthy 100 7726718400 4975233400 1.97 treatment naïve CGPLH369 Preoperative WGS Healthy 100 10967584200 7130956800 2.83 treatment naïve CGPLH37 Preoperative WGS Healthy 100 45970545400 4591328800 12.15 treatment naïve CGPLH370 Preoperative WGS Healthy 100 9237170600 6106373800 2.42 treatment naïve CGPLH371 Preoperative WGS Healthy 100 8077798800 5237070600 2.08 treatment naïve CGPLH380 Preoperative WGS Healthy 100 14049589200 8614241200 3.42 treatment naïve CGPLH381 Preoperative WGS Healthy 100 16743792000 10767882800 4.27 treatment naïve CGPLH382 Preoperative WGS Healthy 100 18474025200 12276437200 4.87 treatment naïve CGPLH383 Preoperative WGS Healthy 100 13215954000 8430420600 3.35 treatment naïve CGPLH384 Preoperative WGS Healthy 100 8481814000 5463636200 2.17 treatment naïve CGPLH385 Preoperative WGS Healthy 100 9596118800 6445445600 2.56 treatment naïve CGPLH386 Preoperative WGS Healthy 100 7399540400 4915484800 1.95 treatment naïve CGPLH387 Preoperative WGS Healthy 100 6860332600 4339724400 1.72 treatment naïve CGPLH388 Preoperative WGS Healthy 100 8679705600 5463945400 2.17 treatment naïve CGPLH389 Preoperative WGS Healthy 100 7266863600 4702386000 1.87 treatment naïve CGPLH390 Preoperative WGS Healthy 100 7509035600 4913901800 1.95 treatment naïve CGPLH391 Preoperative WGS Healthy 100 7252286000 4702404800 1.87 treatment naïve CGPLH392 Preoperative WGS Healthy 100 7302618200 4722407000 1.87 treatment naïve CGPLH393 Preoperative WGS Healthy 100 8879138000 5947871800 2.36 treatment naïve CGPLH394 Preoperative WGS Healthy 100 8737031000 5599777400 2.22 treatment naïve CGPLH395 Preoperative WGS Healthy 100 7783904800 4907146000 1.95 treatment naïve CGPLH396 Preoperative WGS Healthy 100 7585567200 5076638200 2.01 treatment naïve CGPLH398 Preoperative WGS Healthy 100 13001418200 8607025000 3.42 treatment naïve CGPLH399 Preoperative WGS Healthy 100 9867699200 5526646000 2.19 treatment naïve CGPLH400 Preoperative WGS Healthy 100 10573939000 6290438200 2.50 treatment naïve CGPLH401 Preoperative WGS Healthy 100 9415150000 6139638000 2.44 treatment naïve CGPLH402 Preoperative WGS Healthy 100 5541458000 2972027800 1.18 treatment naïve CGPLH403 Preoperative WGS Healthy 100 6470913200 3549772600 1.41 treatment naïve CGPLH404 Preoperative WGS Healthy 100 7369651800 4120205000 1.64 treatment naïve CGPLH405 Preoperative WGS Healthy 100 7360239000 4293522600 1.70 treatment naïve CGPLH406 Preoperative WGS Healthy 100 6028125400 3426007400 1.36 treatment naïve CGPLH407 Preoperative WGS Healthy 100 7073375200 4079286800 1.62 treatment naïve CGPLH408 Preoperative WGS Healthy 100 8006103200 5121285600 2.03 treatment naïve CGPLH409 Preoperative WGS Healthy 100 7343124600 4432335600 1.76 treatment naïve CGPLH410 Preoperative WGS Healthy 100 7551842000 4818779600 1.91 treatment naïve CGPLH411 Preoperative WGS Healthy 100 6119676400 3636478400 1.44 treatment naïve CGPLH412 Preoperative WGS Healthy 100 7960821200 4935752200 1.96 treatment naïve CGPLH413 Preoperative WGS Healthy 100 7623405400 4827888400 1.92 treatment naïve CGPLH414 Preoperative WGS Healthy 100 7381312400 4743337200 1.88 treatment naïve CGPLH415 Preoperative WGS Healthy 100 7240754200 4162208800 1.65 treatment naïve CGPLH416 Preoperative WGS Healthy 100 7745658600 4670226000 1.85 treatment naïve CGPLH417 Preoperative WGS Healthy 100 7627498600 4403085600 1.75 treatment naïve CGPLH418 Preoperative WGS Healthy 100 9090285000 5094814000 2.02 treatment naïve CGPLH419 Preoperative WGS Healthy 100 7914120200 5078389800 2.02 treatment naïve CGPLH42 Preoperative WGS Healthy 100 39492040600 3901039400 10.32 treatment naïve CGPLH420 Preoperative WGS Healthy 100 7014307800 4711393600 1.87 treatment naïve CGPLH422 Preoperative WGS Healthy 100 9103972800 6053559800 2.40 treatment naïve CGPLH423 Preoperative WGS Healthy 100 10154714200 6128800200 2.43 treatment naïve CGPLH424 Preoperative WGS Healthy 100 11002394000 6573756000 2.61 treatment naïve CGPLH425 Preoperative WGS Healthy 100 14681352600 9272557000 3.68 treatment naïve CGPLH426 Preoperative WGS Healthy 100 8336731000 5177430800 2.05 treatment naïve CGPLH427 Preoperative WGS Healthy 100 8242924400 5632991800 2.24 treatment naïve CGPLH428 Preoperative WGS Healthy 100 8512550400 5604756600 2.22 treatment naïve CGPLH429 Preoperative WGS Healthy 100 8369802800 5477121400 2.17 treatment naïve CGPLH43 Preoperative WGS Healthy 100 38513193400 3815698400 10.10 treatment naïve CGPLH430 Preoperative WGS Healthy 100 10357365400 6841611000 2.71 treatment naïve CGPLH431 Preoperative WGS Healthy 100 7599875800 5006909000 1.99 treatment naïve CGPLH432 Preoperative WGS Healthy 100 7932532400 4932304200 1.96 treatment naïve CGPLH434 Preoperative WGS Healthy 100 10417028600 6965998800 2.76 treatment naïve CGPLH435 Preoperative WGS Healthy 100 8747793800 5677115200 2.25 treatment naïve CGPLH436 Preoperative WGS Healthy 100 7990589400 5228737800 2.07 treatment naïve CGPLH437 Preoperative WGS Healthy 100 10156991200 6935537200 2.75 treatment naïve CGPLH438 Preoperative WGS Healthy 100 9473604000 6445455600 2.56 treatment naïve CGPLH439 Preoperative WGS Healthy 100 8303723400 5439877200 2.16 treatment naïve CGPLH440 Preoperative WGS Healthy 100 9055233800 6018631400 2.39 treatment naïve CGPLH441 Preoperative WGS Healthy 100 10290682000 6896415200 2.74 treatment naïve CGPLH442 Preoperative WGS Healthy 100 9876551600 6591249800 2.62 treatment naïve CGPLH443 Preoperative WGS Healthy 100 9837225800 6360740800 2.52 treatment naïve CGPLH444 Preoperative WGS Healthy 100 9199271400 5755941600 2.28 treatment naïve CGPLH445 Preoperative WGS Healthy 100 8089236400 5218259800 2.07 treatment naïve CGPLH446 Preoperative WGS Healthy 100 7890664200 5181606000 2.06 treatment naïve CGPLH447 Preoperative WGS Healthy 100 7775775000 5120239800 2.03 treatment naïve CGPLH448 Preoperative WGS Healthy 100 8686964800 5605079200 2.22 treatment naïve CGPLH449 Preoperative WGS Healthy 100 8604545400 5527726600 2.19 treatment naïve CGPLH45 Preoperative WGS Healthy 100 39029653000 3771601200 9.98 treatment naïve CGPLH450 Preoperative WGS Healthy 100 8428254800 5439950000 2.16 treatment naïve CGPLH451 Preoperative WGS Healthy 100 8128977600 5186265600 2.06 treatment naïve CGPLH452 Preoperative WGS Healthy 100 6474313400 4216316400 1.67 treatment naïve CGPLH453 Preoperative WGS Healthy 100 9831832800 6224917600 2.47 treatment naïve CGPLH455 Preoperative WGS Healthy 100 7373753000 4593473600 1.82 treatment naïve CGPLH456 Preoperative WGS Healthy 100 8455416200 5457148200 2.17 treatment naïve CGPLH457 Preoperative WGS Healthy 100 8647618000 5534503800 2.20 treatment naïve CGPLH458 Preoperative WGS Healthy 100 6633156400 4415186000 1.75 treatment naïve CGPLH459 Preoperative WGS Healthy 100 8361048200 5497193800 2.18 treatment naïve CGPLH46 Preoperative WGS Healthy 100 35361484600 3516232800 9.30 treatment naïve CGPLH460 Preoperative WGS Healthy 100 6788835400 4472282800 1.77 treatment naïve CGPLH463 Preoperative WGS Healthy 100 8534880800 5481759200 2.18 treatment naïve CGPLH464 Preoperative WGS Healthy 100 6692520000 4184463400 1.66 treatment naïve CGPLH465 Preoperative WGS Healthy 100 7772884600 4878430800 1.94 treatment naïve CGPLH466 Preoperative WGS Healthy 100 9056275000 5830877400 2.31 treatment naïve CGPLH467 Preoperative WGS Healthy 100 9331419200 4585861000 1.82 treatment naïve CGPLH468 Preoperative WGS Healthy 100 9334067400 6314830400 2.51 treatment naïve CGPLH469 Preoperative WGS Healthy 100 7376691000 4545246600 1.80 treatment naïve CGPLH47 Preoperative WGS Healthy 100 38485647600 3534883600 9.35 treatment naïve CGPLH470 Preoperative WGS Healthy 100 7899727600 5221650600 2.07 treatment naïve CGPLH471 Preoperative WGS Healthy 100 9200430600 6102371000 2.42 treatment naïve CGPLH472 Preoperative WGS Healthy 100 8143742400 5399946600 2.14 treatment naïve CGPLH473 Preoperative WGS Healthy 100 8123924600 5419825400 2.15 treatment naïve CGPLH474 Preoperative WGS Healthy 100 8853071400 6084059400 2.41 treatment naïve CGPLH475 Preoperative WGS Healthy 100 8115374000 5291718000 2.10 treatment naïve CGPLH476 Preoperative WGS Healthy 100 8163162600 5096869600 2.02 treatment naïve CGPLH477 Preoperative WGS Healthy 100 8350093200 5465468600 2.17 treatment naïve CGPLH478 Preoperative WGS Healthy 100 8259642200 5406516200 2.15 treatment naïve CGPLH479 Preoperative WGS Healthy 100 8027598600 5417376800 2.15 treatment naïve CGPLH48 Preoperative WGS Healthy 100 42232410000 4165893400 11.02 treatment naïve CGPLH480 Preoperative WGS Healthy 100 7832983200 5020127000 1.99 treatment naïve CGPLH481 Preoperative WGS Healthy 100 7578518800 4883280800 1.94 treatment naïve CGPLH482 Preoperative WGS Healthy 100 8279364800 5652263600 2.24 treatment naïve CGPLH483 Preoperative WGS Healthy 100 8660338800 5823859200 2.31 treatment naïve CGPLH484 Preoperative WGS Healthy 100 8445420000 5794328000 2.30 treatment naïve CGPLH485 Preoperative WGS Healthy 100 8371255400 5490207800 2.18 treatment naïve CGPLH486 Preoperative WGS Healthy 100 8216712200 5506871000 2.19 treatment naïve CGPLP487 Preoperative WGS Healthy 100 7936294200 5309250200 2.11 treatment naïve CGPLH488 Preoperative WGS Healthy 100 8355603600 5453160000 2.16 treatment naïve CGPLH49 Preoperative WGS Healthy 100 33912191800 3310056000 8.76 treatment naïve CGPLH490 Preoperative WGS Healthy 100 7768712400 5175567800 2.05 treatment naïve CGPLH491 Preoperative WGS Healthy 100 9070904000 6011275000 2.39 treatment naïve CGPLH492 Preoperative WGS Healthy 100 7208727200 4753213800 1.89 treatment naïve CGPLH493 Preoperative WGS Healthy 100 10542882600 7225870800 2.87 treatment naïve CGPLH494 Preoperative WGS Healthy 100 10908197600 7046645000 2.80 treatment naïve CGPLH495 Preoperative WGS Healthy 100 8945040400 5891697800 2.34 treatment naïve CGPLH496 Preoperative WGS Healthy 100 10859723400 7549608000 3.00 treatment naïve CGPLH497 Preoperative WGS Healthy 100 9630507400 6473162800 2.57 treatment naïve CGPLH498 Preoperative WGS Healthy 100 10060232600 6744622800 2.68 treatment naïve CGPLH499 Preoperative WGS Healthy 100 10221293600 6951282800 2.76 treatment naïve CGPLH50 Preoperative WGS Healthy 100 41243860600 4073272800 10.78 treatment naïve CGPLH500 Preoperative WGS Healthy 100 9703168200 6239893800 2.48 treatment naïve CGPLH501 Preoperative WGS Healthy 100 9104779800 6161602800 2.45 treatment naïve CGPLH502 Preoperative WGS Healthy 100 8514467400 5290881400 2.10 treatment naïve CGPLH503 Preoperative WGS Healthy 100 9019992200 6100383400 2.42 treatment naïve CGPLH504 Preoperative WGS Healthy 100 9320330200 6199750200 2.46 treatment naïve CGPLH505 Preoperative WGS Healthy 100 7499497400 4914559000 1.95 treatment naïve CGPLH506 Preoperative WGS Healthy 100 10526142000 6963312600 2.76 treatment naïve CGPLH507 Preoperative WGS Healthy 100 9091018400 6146678600 2.44 treatment naïve CGPLH508 Preoperative WGS Healthy 100 10989315600 7360201400 2.92 treatment naïve CGPLH509 Preoperative WGS Healthy 100 9729084600 6702691600 2.66 treatment naïve CGPLH51 Preoperative WGS Healthy 100 35967451400 3492833200 9.24 treatment naïve CGPLH510 Preoperative WGS Healthy 100 11162691600 7626795400 3.03 treatment naïve CGPLH511 Preoperative WGS Healthy 100 11888619600 8110427600 3.22 treatment naïve CGPLH512 Preoperative WGS Healthy 100 10726438400 7110078000 2.82 treatment naïve CGPLH513 Preoperative WGS Healthy 100 10701564200 7155271400 2.84 treatment naïve CGPLH514 Preoperative WGS Healthy 100 8822067000 5958773800 2.36 treatment naïve CGPLH515 Preoperative WGS Healthy 100 7792074800 5317464600 2.11 treatment naïve CGPLH516 Preoperative WGS Healthy 100 8642620000 5846439400 2.32 treatment naïve CGPLH517 Preoperative WGS Healthy 100 11915929600 8013937000 3.18 treatment naïve CGPLH518 Preoperative WGS Healthy 100 12804517400 8606661600 3.42 treatment naïve CGPLH519 Preoperative WGS Healthy 100 11513222200 7922798400 3.14 treatment naïve CGPLH52 Preoperative WGS Healthy 100 49247304200 4849631400 12.83 treatment naïve CGPLH520 Preoperative WGS Healthy 100 8942102400 6030683400 2.39 treatment naïve CGPLH54 Preoperative WGS Healthy 100 45399346400 4466164600 11.82 treatment naïve CGPLH55 Preoperative WGS Healthy 100 42547725000 4283337600 11.33 treatment naïve CGPLH56 Preoperative WGS Healthy 100 33460308000 3226338000 8.53 treatment naïve CGPLH57 Preoperative WGS Healthy 100 36504735200 3509125000 9.28 treatment naïve CGPLH59 Preoperative WGS Healthy 100 39642810600 3820011000 10.11 treatment naïve CGPLH625 Preoperative WGS Healthy 100 6408225000 4115487600 1.63 treatment naïve CGPLH626 Preoperative WGS Healthy 100 9915193600 6391657000 2.54 treatment naïve CGPLH63 Preoperative WGS Healthy 100 37447047600 3506737000 9.28 treatment naïve CGPLH639 Preoperative WGS Healthy 100 8158965800 5216049600 2.07 treatment naïve CGPLH64 Preoperative WGS Healthy 100 34275506800 3264508000 8.63 treatment naïve CGPLH640 Preoperative WGS Healthy 100 8058876800 5333551800 2.12 treatment naïve CGPLH642 Preoperative WGS Healthy 100 7545555600 4909732800 1.95 treatment naïve CGPLH643 Preoperative WGS Healthy 100 7865776800 5254772000 2.09 treatment naïve CGPLH644 Preoperative WGS Healthy 100 6890139000 4599387400 1.83 treatment naïve CGPLH646 Preoperative WGS Healthy 100 7757219400 5077408200 2.01 treatment naïve CGPLH75 Preoperative WGS Healthy 100 23882926000 2250344400 5.95 treatment naïve CGPLH76 Preoperative WGS Healthy 100 30631483600 3086042200 8.16 treatment naïve CGPLH77 Preoperative WGS Healthy 100 31651741400 3041290200 8.04 treatment naïve CGPLH78 Preoperative WGS Healthy 100 31165831200 3130079800 8.28 treatment naïve CGPLH79 Preoperative WGS Healthy 100 31935043000 3128408200 8.27 treatment naïve CGPLH80 Preoperative WGS Healthy 100 32965093000 3311371800 8.76 treatment naïve CGPLH81 Preoperative WGS Healthy 100 27035311200 2455084400 6.49 treatment naïve CGPLH82 Preoperative WGS Healthy 100 28447051200 2893358200 7.65 treatment naïve CGPLH83 Preoperative WGS Healthy 100 26702240200 2459494000 6.50 treatment naïve CGPLH84 Preoperative WGS Healthy 100 25176861400 2524467400 6.68 treatment naïve CGPLLU13 Pre-treatment, Day −2 WGS Lung 100 9126585600 5915061800 2.35 Cancer CGPLLU13 Post-treatment, Day 5 WGS Lung 100 7739120200 5071745800 2.01 Cancer CGPLLU13 Post-treatment, Day 28 WGS Lung 100 9081585400 5764371600 2.29 Cancer CGPLLU13 Post-treatment, Day 91 WGS Lung 100 9576557000 6160760200 2.44 Cancer CGPLLU14 Pre-treatment, Day −38 WGS Lung 100 13659198400 9033455800 3.58 Cancer CGPLLU14 Pre-treatment, Day −16 WGS Lung 100 7178855800 4856648600 1.93 Cancer CGPLLU14 Pre-treatment, Day −3 WGS Lung 100 7653473000 4816193600 1.91 Cancer CGPLLU14 Pre-treatment, Day 0 WGS Lung 100 7851997400 5193256600 2.06 Cancer CGPLLU14 Post-treatment, Day 0.33 WGS Lung 100 7193040800 4869701600 1.93 Cancer CGPLLU14 Post-treatment, Day 7 WGS Lung 100 7102050000 4741432600 1.88 Cancer CGPLLU144 Preoperative WGS Lung 100 4934013600 3415936400 1.36 treatment naïve Cancer CGPLLU147 Preoperative WGS Lung 100 24409561000 2118672800 5.61 treatment naïve Cancer CGPLLU161 Preoperative WGS Lung 100 8998813400 6016145000 2.39 treatment naïve Cancer CGPLLU162 Preoperative WGS Lung 100 9709792400 6407866400 2.54 treatment naïve Cancer CGPLLU163 Preoperative WGS Lung 100 9150620200 6063569800 2.41 treatment naïve Cancer CGPLLU165 Preoperative WGS Lung 100 28374436400 2651138600 7.01 treatment naïve Cancer CGPLLU168 Preoperative WGS Lung 100 5692739400 3695191000 1.47 treatment naïve Cancer CGPLLU169 Preoperative WGS Lung 100 9093975600 5805320800 2.30 treatment naïve Cancer CGPLLU175 Preoperative WGS Lung 100 33794816800 3418750400 9.04 treatment naïve Cancer CGPLLU176 Preoperative WGS Lung 100 8778553800 5794950200 2.30 treatment naïve Cancer CGPLLU177 Preoperative WGS Lung 100 3734614800 2578696200 1.02 treatment naïve Cancer CGPLLU180 Preoperative WGS Lung 100 28305936600 2756034200 7.29 treatment naïve Cancer CGPLLU198 Preoperative WGS Lung 100 23244959200 2218577200 5.86 treatment naïve Cancer CGPLLU202 Preoperative WGS Lung 100 21110128200 1831279400 4.84 treatment naïve Cancer CGPLLU203 Preoperative WGS Lung 100 4304235600 2896429000 1.15 treatment naïve Cancer CGPLLU205 Preoperative WGS Lung 100 10502467000 7386984800 2.93 treatment naïve Cancer CGPLLU206 Preoperative WGS Lung 100 21888248200 2026666000 5.36 treatment naïve Cancer CGPLLU207 Preoperative WGS Lung 100 10806230600 7363049000 2.92 treatment naïve Cancer CGPLLU208 Preoperative WGS Lung 100 7795426800 5199545800 2.06 treatment naïve Cancer CGPLLU209 Preoperative WGS Lung 100 26174542000 2621961800 6.93 treatment naïve Cancer CGPLLU244 Pre-treatment, Day −7 WGS Lung 100 9967531400 6704365800 2.66 Cancer CGPLLU244 Pre-treatment, Day −1 WGS Lung 100 9547119200 5785172600 2.30 Cancer CGPLLU244 Post-treatment Day 6 WGS Lung 100 9535898600 6452174000 2.56 Cancer CGPLLU244 Post-treatment, Day 62 WGS Lung 100 8783628600 5914149000 2.35 Cancer CGPLLU245 Pre-treatment, Day −32 WGS Lung 100 10025823200 6313303800 2.51 Cancer CGPLLU245 Pre-treatment, Day 0 WGS Lung 100 9462480400 6612867800 2.62 Cancer CGPLLU245 Post-treatment, Day 7 WGS Lung 100 9143825000 6431013200 2.55 Cancer CGPLLU245 Post-treatment, Day 21 WGS Lung 100 9072713800 6368533000 2.53 Cancer CGPLLU246 Pre-treatment, Day −21 WGS Lung 100 9579787000 6458003400 2.56 Cancer CGPLLU246 Pre-treatment, Day 0 WGS Lung 100 9512703600 6440535600 2.56 Cancer CGPLLU246 Post-treatment, Day 9 WGS Lung 100 9512646000 6300939200 2.50 Cancer CGPLLU246 Post-treatment, Day 42 WGS Lung 100 11136103000 7358747400 2.92 Cancer CGPLLU264 Pre-treatment, Day −1 WGS Lung 100 9196005000 6239803600 2.48 Cancer CGPLLU264 Post-treatment, Day 6 WGS Lung 100 8247416600 5600454200 2.22 Cancer CGPLLU264 Post-treatment, Day 27 WGS Lung 100 8681022200 5856109000 2.32 Cancer CGPLLU264 Post-treatment, Day 69 WGS Lung 100 8931976400 5974246000 2.37 Cancer CGPLLU265 Pre-treatment, Day 0 WGS Lung 100 9460534000 6111185200 2.43 Cancer CGPLLU265 Post-treatment, Day 3 WGS Lung 100 8051601200 4984166600 1.98 Cancer CGPLLU265 Post-treatment, Day 7 WGS Lung 100 8082224600 5110092600 2.03 Cancer CGPLLU265 Post-treatment, Day 84 WGS Lung 100 8368637400 5369526400 2.13 Cancer CGPLLU266 Pre-treatment, Day 0 WGS Lung 100 8583766400 5846473600 2.32 Cancer CGPLL266 Post-treatment, Day 16 WGS Lung 100 8795793600 5984531400 2.37 Cancer CGPLLU266 Post-treatment, Day 83 WGS Lung 100 9157947600 6227735000 2.47 Cancer CGPLLU266 Post-treatment, Day 328 WGS Lung 100 7299455400 5049379000 2.00 Cancer CGPLLU267 Pre-treatment, Day −1 WGS Lung 100 10658657800 6892067000 2.73 Cancer CGPLLU267 Post-treatment, Day 34 WGS Lung 100 8492833400 5101097800 2.02 Cancer CGPLLU267 Post-treatment, Day 90 WGS Lung 100 12030314800 7757930400 3.08 Cancer CGPLLU269 Pre-treatment, Day 0 WGS Lung 100 9170168000 5830454400 2.31 Cancer CGPLLU269 Post-treatment, Day 9 WGS Lung 100 8905640400 5298461400 2.10 Cancer CGPLLU269 Post-treatment, Day 28 WGS Lung 100 8455306600 5387927400 2.14 Cancer CGPLLU271 Post-treatment, Day 259 WGS Lung 100 8112060400 5404979000 2.14 Cancer CGPLLU271 Pre-treatment, Day 0 WGS Lung 100 13150818200 8570453400 3.40 Cancer CGPLLU271 Post-treatment, Day 6 WGS Lung 100 9008880600 5854051400 2.32 Cancer CGPLLU271 Post-treatment, Day 20 WGS Lung 100 8670913000 5461577000 2.17 Cancer CGPLLU271 Post-treatment, Day 104 WGS Lung 100 8887441400 5609039000 2.23 Cancer CGPLLU43 Pre-treatment, Day −1 WGS Lung 100 8407811200 5203486400 2.06 Cancer CGPLLU43 Post-treatment, Day 6 WGS Lung 100 9264335200 5626714400 2.23 Cancer CGPLLU43 Post-treatment, Day 27 WGS Lung 100 8902283000 5485656200 2.18 Cancer CGPLLU43 Post-treatment, Day 83 WGS Lung 100 9201509200 5075084200 2.33 Cancer CGPLLU86 Pre-treatment, Day 0 WGS Lung 100 9152729200 6248173200 2.48 Cancer CGPLLU86 Post-treatment, Day 0.5 WGS Lung 100 6703253000 4663026800 1.85 Cancer CGPLLU86 Post-treatment, Day 7 WGS Lung 100 6590121400 4559562400 1.81 Cancer CGPLLU86 Post-treatment, Day 17 WGS Lung 100 8653551800 5900136000 2.34 Cancer CGPLLU88 Pre-treatment, Day 0 WGS Lung 100 8096528000 5505475400 2.18 Cancer CGPLLU88 Post-treatment, Day 7 WGS Lung 100 8283192200 5784217600 2.30 Cancer CGPLLU88 Post-treatment, Day 297 WGS Lung 100 9297110800 6407258000 2.54 Cancer CGPLLU89 Pre-treatment, Day 0 WGS Lung 100 7842145200 5356095400 2.13 Cancer CGPLLU89 Post-treatment, Day 7 WGS Lung 100 7234220200 4930375200 1.96 Cancer CGPLLU89 Post-treatment, Day 22 WGS Lung 100 6242889800 4057361000 1.61 Cancer CGPLOV11 Preoperative WGS Ovarian 100 8985130400 5871959600 2.33 treatment naïve Cancer CGPLOV12 Preoperative WGS Ovarian 100 9705820000 6430505400 2.55 treatment naïve Cancer CGPLOV13 Preoperative WGS Ovarian 100 10307949490 7029712000 2.79 treatment naïve Cancer CGPLOV15 Preoperative WGS Ovarian 100 8472829400 5562142400 2.21 treatment naïve Cancer CGPLOV16 Preoperative WGS Ovarian 100 10977781000 7538581600 2.99 treatment naïve Cancer CGPLOV19 Preoperative WGS Ovarian 100 8800876200 5855304000 2.32 treatment naïve Cancer CGPLOV20 Preoperative WGS Ovarian 100 8714443600 5695165800 2.26 treatment naïve Cancer CGPLOV21 Preoperative WGS Ovarian 100 10180394800 7120260400 2.83 treatment naïve Cancer CGPLOV22 Preoperative WGS Ovarian 100 10107760000 6821916800 2.71 treatment naïve Cancer CGPLOV23 Preoperative WGS Ovarian 100 10643399800 7206330800 2.86 treatment naïve Cancer CGPLOV24 Preoperative WGS Ovarian 100 6780929000 4623300400 1.83 treatment naïve Cancer CGPLOV25 Preoperative WGS Ovarian 100 7817548600 5359975200 2.13 treatment naïve Cancer CGPLOV26 Preoperative WGS Ovarian 100 11763101400 8178024400 3.25 treatment naïve Cancer CGPLOV28 Preoperative WGS Ovarian 100 9522546400 6259423400 2.48 treatment naïve Cancer CGPLOV31 Preoperative WGS Ovarian 100 9104831200 6109358400 2.42 treatment naïve Cancer CGPLOV32 Preoperative WGS Ovarian 100 9222073600 6035150000 2.39 treatment naïve Cancer CGPLOV37 Preoperative WGS Ovarian 100 8898328600 5971018200 2.37 treatment naïve Cancer CGPLOV38 Preoperative WGS Ovarian 100 8756025200 5861536600 2.33 treatment naïve Cancer CGPLOV40 Preoperative WGS Ovarian 100 9709391600 6654707200 2.64 treatment naïve Cancer CGPLOV41 Preoperative WGS Ovarian 100 8923625000 5973070400 2.37 treatment naïve Cancer CGPLOV42 Preoperative WGS Ovarian 100 10719380400 7353214200 2.92 treatment naïve Cancer CGPLOV43 Preoperative WGS Ovarian 100 10272189000 6423288600 2.55 treatment naïve Cancer CGPLOV44 Preoperative WGS Ovarian 100 9861862600 6769185800 2.69 treatment naïve Cancer CGPLOV46 Preoperative WGS Ovarian 100 8788956400 5789863400 2.30 treatment naïve Cancer CGPLOV47 Preoperative WGS Ovarian 100 9380561800 6480763600 2.57 treatment naïve Cancer CGPLOV48 Preoperative WGS Ovarian 100 9258552600 6380106400 2.53 treatment naïve Cancer CGPLOV49 Preoperative WGS Ovarian 100 8787025400 6134503600 2.43 treatment naïve Cancer CGPLOV50 Preoperative WGS Ovarian 100 10144154400 6984721400 2.77 treatment naïve Cancer CGPLPA112 Preoperative WGS Pancreatic 100 12740651400 9045622000 3.59 treatment naïve Cancer CGPLPA113 Preoperative WGS Duodenal 100 8802479000 5909030800 2.34 treatment naïve Cancer CGPLPA114 Preoperative WGS Bile Duct 100 8792313600 6019061000 2.39 treatment naïve Cancer CGPLPA115 Preoperative WGS Bile Duct 100 8636551400 5958809000 2.36 treatment naïve Cancer CGPLPA117 Preoperative WGS Bile Duct 100 9128885200 6288833200 2.50 treatment naïve Cancer CGPLPA118 Preoperative WGS Bile Duct 100 7931485800 5407532800 2.15 treatment naïve Cancer CGPLPA122 Preoperative WGS Bile Duct 100 10888985000 7530118800 2.99 treatment naïve Cancer CGPLPA124 Preoperative WGS Bile Duct 100 8562012400 5860171000 2.33 treatment naïve Cancer CGPLPA125 Preoperative WGS Bile Duct 100 9715576600 6390321000 2.54 treatment naïve Cancer CGPLPA126 Preoperative WGS Bile Duct 100 8056768800 5651600800 2.24 treatment naïve Cancer CGPLPA127 Preoperative WGS Bile Duct 100 8000301000 5382987600 2.14 treatment naïve Cancer CGPLPA128 Preoperative WGS Bile Duct 100 6165751600 4256521400 1.69 treatment naïve Cancer CGPLPA129 Preoperative WGS Bile Duct 100 7143147400 4917370400 1.95 treatment naïve Cancer CGPLPA130 Preoperative WGS Bile Duct 100 5664035000 3603919400 1.43 treatment naïve Cancer CGPLPA131 Preoperative WGS Bile Duct 100 8292982000 5844942000 2.32 treatment naïve Cancer CGPLPA134 Preoperative WGS Bile Duct 100 7088917000 5048887600 2.00 treatment naïve Cancer CGPLPA135 Preoperative WGS Bile Duct 100 8759665600 5800618200 2.30 treatment naïve Cancer CGPLPA136 Preoperative WGS Bile Duct 100 7539715800 5248227600 2.08 treatment naïve Cancer CGPLPA137 Preoperative WGS Bile Duct 100 8391815400 5901273800 2.34 treatment naïve Cancer CGPLPA139 Preoperative WGS Bile Duct 100 8992280200 6328314400 2.51 treatment naïve Cancer CGPLPA14 Preoperative WGS Pancreatic 100 8787706200 5731317600 2.27 treatment naïve Cancer CGPLPA140 Preoperative WGS Bile Duct 100 16365641800 11216732000 4.45 treatment naïve Cancer CGPLPA141 Preoperative WGS Bile Duct 100 15086298000 10114790200 4.01 treatment naïve Cancer CGPLPA15 Preoperative WGS Pancreatic 100 8255566800 5531677600 2.20 treatment naïve Cancer CGPLPA155 Preoperative WGS Bile Duct 100 9457155800 6621881800 2.63 treatment naïve Cancer CGPLPA156 Preoperative WGS Pancreatic 100 9345385800 6728653000 2.67 treatment naïve Cancer CGPLPA165 Preoperative WGS Bile Duct 100 8356604600 5829895800 2.31 treatment naïve Cancer CGPLPA168 Preoperative WGS Bile Duct 100 10355661600 7048115500 2.80 treatment naïve Cancer CGPLPA17 Preoperative WGS Pancreatic 100 8073547400 4687808000 1.86 treatment naïve Cancer CGPLPA184 Preoperative WGS Bile Duct 100 9014218400 6230922200 2.47 treatment naïve Cancer CGPLPA187 Preoperative WGS Bile Duct 100 8883536200 6140874400 2.44 treatment naïve Cancer CGPLPA23 Preoperative WGS Pancreatic 100 9335452000 6246525400 2.48 treatment naïve Cancer CGPLPA25 Preoperative WGS Pancreatic 100 10077515400 6103322200 2.42 treatment naïve Cancer CGPLPA26 Preoperative WGS Pancreatic 100 8354272400 5725781000 2.27 treatment naïve Cancer CGPLPA28 Preoperative WGS Pancreatic 100 8477461600 5688846800 2.26 treatment naïve Cancer CGPLPA33 Preoperative WGS Pancreatic 100 7287615600 4596723800 1.82 treatment naïve Cancer CGPLPA34 Preoperative WGS Pancreatic 100 6122902400 4094828000 1.62 treatment naïve Cancer CGPLPA37 Preoperative WGS Pancreatic 100 12714888200 8527779200 3.38 treatment naïve Cancer CGPLPA38 Preoperative WGS Pancreatic 100 8525500600 5501341400 2.18 treatment naïve Cancer CGPLPA39 Preoperative WGS Pancreatic 100 10602663600 6812333000 2.70 treatment naïve Cancer CGPLPA40 Preoperative WGS Pancreatic 100 9083670000 5394717800 2.14 treatment naïve Cancer CGPLPA42 Preoperative WGS Pancreatic 100 5972126600 3890395200 1.54 treatment naïve Cancer CGPLPA46 Preoperative WGS Pancreatic 100 4720090200 2626298800 1.04 treatment naïve Cancer CGPLPA47 Preoperative WGS Pancreatic 100 7317385800 4543833000 1.80 treatment naïve Cancer CGPLPA48 Preoperative WGS Pancreatic 100 7553856200 5022695600 1.99 treatment naïve Cancer CGPLPA52 Preoperative WGS Pancreatic 100 5655875000 3551861600 1.41 treatment naïve Cancer CGPLPA53 Preoperative WGS Pancreatic 100 9504749000 6323344800 2.51 treatment naïve Cancer CGPLPA58 Preoperative WGS Pancreatic 100 8088090200 5118138200 2.03 treatment naïve Cancer CGPLPA59 Preoperative WGS Pancreatic 100 14547364600 9617778600 3.82 treatment naïve Cancer CGPLPA67 Preoperative WGS Pancreatic 100 8222177400 5351172600 2.12 treatment naïve Cancer CGPLPA69 Preoperative WGS Pancreatic 100 7899181400 5006114800 1.99 treatment naïve Cancer CGPLPA71 Preoperative WGS Pancreatic 100 7349620400 4955417400 1.97 treatment naïve Cancer CGPLPA74 Preoperative WGS Pancreatic 100 6666371400 4571394200 1.81 treatment naïve Cancer CGPLPA76 Preoperative WGS Pancreatic 100 9755658600 6412606800 2.54 treatment naïve Cancer CGPLPA85 Preoperative WGS Pancreatic 100 10856223000 7309498600 2.90 treatment naïve Cancer CGPLPA86 Preoperative WGS Pancreatic 100 8744365400 5514523200 2.19 treatment naïve Cancer CGPLPA92 Preoperative WGS Pancreatic 100 8073791200 5390492800 2.14 treatment naïve Cancer CGPLPA93 Preoperative WGS Pancreatic 100 10390273000 7186589400 2.85 treatment naïve Cancer CGPLPA94 Preoperative WGS Pancreatic 100 11060347600 7641336400 3.03 treatment naïve Cancer CGPLPA95 Preoperative WGS Pancreatic 100 12416627200 7206503800 2.86 treatment naïve Cancer CGST102 Preoperative WGS Gastric 100 6637004600 4545072600 1.80 treatment naïve cancer CGST11 Preoperative WGS Gastric 100 9718427800 6259679600 2.48 treatment naïve cancer CGST110 Preoperative WGS Gastric 100 9319661600 6359317400 2.52 treatment naïve cancer CGST114 Preoperative WGS Gastric 100 6865213000 4841171600 1.92 treatment naïve cancer CGST13 Preoperative WGS Gastric 100 9284554800 6360843800 2.52 treatment naïve cancer CGST131 Preoperative WGS Gastric 100 5924382000 3860677200 1.53 treatment naïve cancer CGST141 Preoperative WGS Gastric 100 8486380800 5860491000 2.33 treatment naïve cancer CGST16 Preoperative WGS Gastric 100 13820725800 9377828000 3.72 treatment naïve cancer CGST18 Preoperative WGS Gastric 100 7781288000 5278862400 2.09 treatment naïve cancer CGST21 Preoperative WGS Gastric 100 7171165400 4103970800 1.63 treatment naïve cancer CGST26 Preoperative WGS Gastric 100 8983961800 6053405600 2.40 treatment naïve cancer CGST28 Preoperative WGS Gastric 100 9683035400 6745116400 2.68 treatment naïve cancer CGST30 Preoperative WGS Gastric 100 8584086600 5741416000 2.28 treatment naïve cancer CGST32 Preoperative WGS Gastric 100 8568194600 5783369200 2.29 treatment naïve cancer CGST33 Preoperative WGS Gastric 100 9351699600 6448718400 2.56 treatment naïve cancer CGST38 Preoperative WGS Gastric 100 8409876400 5770989200 2.29 treatment naïve cancer CGST39 Preoperative WGS Gastric 100 10573763000 7597016000 3.01 treatment naïve cancer CGST41 Preoperative WGS Gastric 100 9434854200 6609415400 2.62 treatment naïve cancer CGST45 Preoperative WGS Gastric 100 8203868600 5625223000 2.23 treatment naïve cancer CGST47 Preoperative WGS Gastric 100 8938597600 6178990600 2.45 treatment naïve cancer CGST48 Preoperative WGS Gastric 100 9106628800 6517085200 2.59 treatment naïve cancer CGST53 Preoperative WGS Gastric 100 9005374200 5854996200 2.32 treatment naïve cancer CGST58 Preoperative WGS Gastric 100 10020368600 6133458400 2.43 treatment naïve cancer CGST67 Preoperative WGS Gastric 100 9198135600 5911071000 2.35 treatment naïve cancer CGST77 Preoperative WGS Gastric 100 8228789400 5119116800 2.03 treatment naïve cancer CGST80 Preoperative WGS Gastric 100 10596963400 7283152800 2.89 treatment naïve cancer CGST81 Preoperative WGS Gastric 100 8494881200 5838064000 2.32 treatment naïve cancer

TABLE 5 APPENDIX E: High coverage whole genome cfDNA analyses of healthy individuals and lung cancer patients Correlation Correlation of Correction of Fragment GC Corrected of Fragment Correlation Ratio Profile Fragment Ratio Ratio Profile of Fragment to Median Profile to to Median Ratio Median Fragment Median Fragment Fragment Profile to cfDNA Ratio Profile Ratio Profile Ratio Lymphocyte Patient Analysis Stage at Fragment of Healthy of Healthy Profile of Nucleosome Patient Type Type Timepoint Diagnosis Size (bp) Individuals Individuals Lymphocytes Distances CGPLH75 Healthy WGS Preoperative NA 168 0.977 0.952 0.920 −0.886 treatment naïve CGPLH77 Healthy WGS Preoperative NA 166 0.970 0.960 0.904 −0.912 treatment naïve CGPLH80 Healthy WGS Preoperative NA 168 0.955 0.949 0.960 −0.917 treatment naïve CGPLH81 Healthy WGS Preoperative NA 167 0.949 0.953 0.869 −0.883 treatment naïve CGPLH82 Healthy WGS Preoperative NA 166 0.969 0.949 0.954 −0.917 treatment naïve CGPLH83 Healthy WGS Preoperative NA 167 0.949 0.939 0.919 −0.904 treatment naïve CGPLH84 Healthy WGS Preoperative NA 168 0.967 0.948 0.951 −0.913 treatment naïve CGPLH52 Healthy WGS Preoperative NA 167 0.946 0.968 0.952 −0.924 treatment naïve CGPLH35 Healthy WGS Preoperative NA 166 0.981 0.973 0.945 −0.921 treatment naïve CGPLH37 Healthy WGS Preoperative NA 168 0.968 0.970 0.951 −0.922 treatment naïve CGPLH54 Healthy WGS Preoperative NA 167 0.968 0.976 0.948 −0.925 treatment naïve CGPLH55 Healthy WGS Preoperative NA 166 0.947 0.964 0.948 −0.917 treatment naïve CGPLH48 Healthy WGS Preoperative NA 168 0.959 0.965 0.960 −0.923 treatment naïve CGPLH50 Healthy WGS Preoperative NA 167 0.960 0.968 0.952 −0.921 treatment naïve CGPLH36 Healthy WGS Preoperative NA 168 0.955 0.954 0.955 −0.919 treatment naïve CGPLH42 Healthy WGS Preoperative NA 167 0.973 0.963 0.948 −0.918 treatment naïve CGPLH43 Healthy WGS Preoperative NA 166 0.952 0.958 0.953 −0.928 treatment naïve CGPLH69 Healthy WGS Preoperative NA 168 0.970 0.965 0.951 −0.925 treatment naïve CGPLH45 Healthy WGS Preoperative NA 168 0.965 0.950 0.949 −0.911 treatment naïve CGPLH47 Healthy WGS Preoperative NA 167 0.952 0.944 0.954 −0.921 treatment naïve CGPLH46 Healthy WGS Preoperative NA 168 0.966 0.965 0.953 −0.923 treatment naïve CGPLH63 Healthy WGS Preoperative NA 168 0.977 0.968 0.939 −0.920 treatment naïve CGPLH51 Healthy WGS Preoperative NA 168 0.935 0.955 0.957 −0.914 treatment naïve CGPLH57 Healthy WGS Preoperative NA 169 0.965 0.954 0.955 −0.917 treatment naïve CGPLH49 Healthy WGS Preoperative NA 168 0.958 0.951 0.950 −0.924 treatment naïve CGPLH56 Healthy WGS Preoperative NA 166 0.940 0.957 0.959 −0.911 treatment naïve CGPLH64 Healthy WGS Preoperative NA 169 0.960 0.940 0.949 −0.918 treatment naïve CGPLH78 Healthy WGS Preoperative NA 166 0.956 0.936 0.958 −0.911 treatment naïve CGPLH79 Healthy WGS Preoperative NA 168 0.960 0.957 0.953 −0.917 treatment naïve CGPLH76 Healthy WGS Preoperative NA 167 0.969 0.965 0.953 −0.917 treatment naïve CGPLLU175 Lung WGS Preoperative I 165 0.316 0.284 0.244 −0.262 Cancer treatment naïve CGPLLU180 Lung WGS Preoperative I 166 0.907 0.846 0.826 −0.819 Cancer treatment naïve CGPLLU198 Lung WGS Preoperative I 166 0.972 0.946 0.928 −0.911 Cancer treatment naïve CGPLLU202 Lung WGS Preoperative I 163 0.821 0.605 0.905 −0.843 Cancer treatment naïve CGPLLU165 Lung WGS Preoperative II 163 0.924 0.961 0.815 −0.851 Cancer treatment naïve CGPLLU209 Lung WGS Preoperative II 163 0.578 0.526 0.513 −0.534 Cancer treatment naïve CGPLLU147 Lung WGS Preoperative III 166 0.953 0.919 0.939 −0.912 Cancer treatment naïve CGPLLU206 Lung WGS Preoperative III 158 0.488 0.343 0.460 −0.481 Cancer treatment naïve

TABLE 6 APPENDIX F: Monitoring response to therapy using whole genome analyses of cfDNA fragmentation profiles and targeted mutations analyses Correlation or Fragment Correlation Ratio Profile of Fragment to Median Ratio Progression- Fragment Profile to Maximum free Ratio Profile Lymphocyte Mutant Patient Survival of Healthy Nucleosome Targeted Allele Patient Type Analysis Type Timepoint Stage (months) Individuals Distances Mutation Fraction CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.941 −0.841 EGFR 861L>Q 0.89% Cancer Analysis and WGS Day −38 CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.933 −0.833 EGFR 861L>Q 0.18% Cancer Analysis and WGS Day −16 CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.908 −0.814 EGFR 719G>S 0.49% Cancer Analysis and WGS Day 3 CGPLLU14 Lung Targeted Mutation Pre-treatment, IV 15.4 0.883 −0.752 EGFR 861L>Q 1.39% Cancer Analysis and WGS Day 0 CGPLLU14 Lung Targeted Mutation Post-treatment, IV 15.4 0.820 −0.692 EGFR 719G>S 1.05% Cancer Analysis and WGS Day 0.33 CGPLLU14 Lung Targeted Mutation Post-treatment, IV 15.4 0.927 −0.887 EGFR 861L>Q 0.00% Cancer Analysis and WGS Day 7 CGPLLU88 Lung Targeted Mutation Pre-treatment, IV 18.0 0.657 −0.584 EGFR 9.06% Cancer Analysis and WGS Day 0 745KELREA>T CGPLLU88 Lung Targeted Mutation Post-treatment, IV 18.0 0.939 −0.799 EGFR 790T>M 0.15% Cancer Analysis and WGS Day 7 CGPLLU88 Lung Targeted Mutation Post-treatment, IV 18.0 0.946 −0.869 EGFR 0.93% Cancer Analysis and WGS Day 297 745KELREA>T CGPLLU244 Lung Targeted Mutation Pre-treatment, IV 1.2 0.850 −0.706 EGFR 858L>R 4.98% Cancer Analysis and WGS Day −7 CGPLLU244 Lung Targeted Mutation Pre-treatment, IV 1.2 0.867 −0.764 EGFR 62L>R 3.41% Cancer Analysis and WGS Day −1 CGPLLU244 Lung Targeted Mutation Post-treatment, IV 1.2 0.703 −0.639 EGFR 858L>R 5.57% Cancer Analysis and WGS Day 6 GGPLLU244 Lung Targeted Mutation Post-treatment, IV 1.2 0.659 −0.660 EGFR 858L>R 11.80% Cancer Analysis and WGS Day 82 CGPLLU245 Lung Targeted Mutation Pre-treatment, IV 1.7 0.871 −0.724 EGFR 10.60% Cancer Analysis and WGS Day −32 745KELREA>K CGPLLU245 Lung Targeted Mutation Pre-treatment, IV 1.7 0.736 −0.608 EGFR 14.10% Cancer Analysis and WGS Day 0 745KELREA>K CGPLLU245 Lung Targeted Mutation Post-treatment, IV 1.7 0.731 −0.559 EGFR 6.56% Cancer Analysis and WGS Day 7 745KELREA>K CGPLLU245 Lung Targeted Mutation Post-treatment, IV 1.7 0.613 −0.426 EGFR 10.69% Cancer Analysis and WGS Day 21 745KELREA>K CGPLLU246 Lung Targeted Mutation Pre-treatment, IV 1.3 0.897 −0.757 EGFR 790T>M 0.49% Cancer Analysis and WGS Day −21 CGPLLU246 Lung Targeted Mutation Pre-treatment, IV 1.3 0.469 0.376 EGFR 858L>R 6.17% Cancer Analysis and WGS Day 0 CGPLLU246 Lung Targeted Mutation Post-treatment, IV 1.3 0.874 −0.746 EGFR 858L>R 1.72% Cancer Analysis and WGS Day 9 CGPLLU246 Lung Targeted Mutation Post-treatment, IV 1.3 0.775 −0.665 EGFR 858L>R 5.29% Cancer Analysis and WGS Day 42 CGPLLU86 Lung Targeted Mutation Pre-treatment, IV 12.4 0.817 −0.630 EGFR 0.00% Cancer Analysis and WGS Day 0 746ELREATS>D CGPLLU86 Lung Targeted Mutation Post-treatment, IV 12.4 0.916 −0.811 EGER 0.19% Cancer Analysis and WGS Day 0.5 746ELREATS>D CGPLLU86 Lung Targeted Mutation Post-treatment, IV 12.4 0.859 −0.694 EGFR 0.00% Cancer Analysis and WGS Day 7 746ELREATS>D CGPLLU86 Lung Targeted Mutation Post-treatment, IV 12.4 0.932 −0.848 EGFR 0.00% Cancer Analysis and WGS Day 17 746ELREATS>D CGPLLU89 Lung Targeted Mutation Pre-treatment, IV 6.7 0.864 −0.729 EGFR 0.42% Cancer Analysis and WGS Day 0 747LREATS>— CGPLLU89 Lung Targeted Mutation Post-treatment, IV 6.7 0.908 −0.803 EGFR 0.20% Cancer Analysis and WGS Day 7 747LREATS>— CGPLLU89 Lung Targeted Mutation Post-treatment, IV 6.7 0.853 −0.881 EGFR 0.00% Cancer Analysis and WGS Day 22 747LREATS>— CGLU316 Lung Targeted Mutation Pre-treatment, IV 1.4 0.331 −0.351 EGFR L861Q 15.72% Cancer Analysis and WGS Day −53 CGLU316 Lung Targeted Mutation Pre-treatment, IV 1.4 0.225 −0.253 EGFR L861Q 45.67% Cancer Analysis and WGS Day −4 CGLU316 Lung Targeted Mutation Post-treatment, IV 1.4 0.336 −0.364 EGFR G719A 33.38% Cancer Analysis and WGS Day 18 CGLU316 Lung Targeted Mutation Post-treatment, IV 1.4 0.340 −0.364 EGFR L861Q 66.01% Cancer Analysis and WGS Day 87 CGLU344 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.935 −0.818 EGFR 0.00% Cancer Analysis and WGS Day −21 E746_A750del CGLU344 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.919 −0.774 EGFR 0.22% Cancer Analysis and WGS Day 0 E746_A750del CGLU344 Lung Targeted Mutation Post-treatment, IV Ongoing 0.953 −0.860 EGFR 0.40% Cancer Analysis and WGS Day 0.1675 E746_A750del CGLU344 Lung Targeted Mutation Post-treatment, IV Ongoing 0.944 −0.832 EGFR 0.00% Cancer Analysis and WGS Day 59 E746_A750del CGLU369 Lung Targeted Mutation Pre-treatment, IV 7.5 0.825 −0.826 EGFR L858R 20.61% Cancer Analysis and WGS Day −2 CGLU369 Lung Targeted Mutation Post-treatment, IV 7.5 0.950 −0.903 EGFR L858R 0.22% Cancer Analysis and WGS Day 12 CGLU369 Lung Targeted Mutation Post-treatment, IV 7.5 0.945 −0.889 EGFR L858R 0.16% Cancer Analysis and WGS Day 68 CGLU369 Lung Targeted Mutation Post-treatment, IV 7.5 0.886 −0.883 EGFR L858R 0.10% Cancer Analysis and WGS Day 110 CGLU373 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.922 −0.804 EGFR 0.82% Cancer Analysis and WGS Day −2 E746_A750del CGLU373 Lung Targeted Mutation Post-treatment, IV Ongoing 0.959 −0.853 EGFR 0.00% Cancer Analysis and WGS Day 0.125 E746_A750del CGLU373 Lung Targeted Mutation Post-treatment, IV Ongoing 0.967 −0.886 EGFR 0.15% Cancer Analysis and WGS Day 7 E746_A750del CGLU373 Lung Targeted Mutation Post-treatment, IV Ongoing 0.951 −0.890 EGFR 0.00% Cancer Analysis and WGS Day 47 E746_A750del CGPLLU13 Lung Targeted Mutation Pre-treatment, IV 1.5 0.425 −0.400 EGFR 7.66% Cancer Analysis and WGS Day −2 E746_A750del CGPLLU13 Lung Targeted Mutation Post-treatment, IV 1.5 0.272 −0.257 EGFR 13.10% Cancer Analysis and WGS Day 5 E746_A750del CGPLLU13 Lung Targeted Mutation Post-treatment, IV 1.5 0.584 −0.536 EGFR 6.09% Cancer Analysis and WGS Day 28 E746_A750del CGPLLU13 Lung Targeted Mutation Post-treatment, IV 1.5 0.530 −0.513 EGFR 9.28% Cancer Analysis and WGS Day 91 E746_A750del CGPLLU264 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.946 −0.824 EGFR D761N 0.00% Cancer Analysis and WGS Day −1 CGPLLU264 Lung Targeted Mutation Post-treatment, IV Ongoing 0.927 −0.788 EGFR D761N 0.16% Cancer Analysis and WGS Day 6 CGPLLU264 Lung Targeted Mutation Post-treatment, IV Ongoing 0.962 −0.856 EGFR D761N 0.00% Cancer Analysis and WGS Day 27 CGPLLU264 Lung Targeted Mutation Post-treatment, IV Ongoing 0.960 −0.894 EGFR D76IN 0.00% Cancer Analysis and WGS Day 69 CGPLLU265 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.953 −0.859 EGFR L858R 0.21% Cancer Analysis and WGS Day 0 CGPLLU265 Lung Targeted Mutation Post-treatment, IV Ongoing 0.949 −0.842 EGFR L858R 0.21% Cancer Analysis and WGS Day 3 CGPLLU265 Lung Targeted Mutation Post-treatment, IV Ongoing 0.955 −0.844 EGFR T790M 0.21% Cancer Analysis and WGS Day 7 CGPLLU265 Lung Targeted Mutation Post-treatment, IV Ongoing 0.946 −0.825 EGFR L858R 0.00% Cancer Analysis and WGS Day 84 CGPLLU266 Lung Targeted Mutation Pre-treatment, IV 9.6 0.961 −0.904 NA 0.00% Cancer Analysis and WGS Day 0 CGPLLU266 Lung Targeted Mutation Post-treatment, IV 9.6 0.959 −0.886 NA 0.00% Cancer Analysis and WGS Day 16 CGPLLU266 Lung Targeted Mutation Post-treatment, IV 9.6 0.961 −0.880 NA 0.00% Cancer Analysis and WGS Day 83 CGPLLU266 Lung Targeted Mutation Post-treatment, IV 9.6 0.958 −0.855 NA 0.00% Cancer Analysis and WGS Day 328 CGPLLU267 Lung Targeted Mutation Pre-treatment, IV 3.9 0.919 −0.863 EGFR L858R 1.93% Cancer Analysis and WGS Lay −1 CGPLLU267 Lung Targeted Mutation Post-treatment, IV 3.9 0.363 −0.889 EGFR L858R 0.14% Cancer Analysis and WGS Day 34 CGPLLU267 Lung Targeted Mutation Post-treatment, IV 3.9 0.962 −0.876 EGFR L858R 0.38% Cancer Analysis and WGS Day 90 CGPLLU269 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.951 −0.864 EGFR L858R 0.10% Cancer Analysis and WGS Day 0 CGPLLU269 Lung Targeted Mutation Post-treatment, IV Ongoing 0.941 −0.894 EGFR L858R 0.00% Cancer Analysis and WGS Day 9 CGPLLU269 Lung Targeted Mutation Post-treatment, IV Ongoing 0.957 −0.876 EGFR L858R 0.00% Cancer Analysis and WGS Day 28 CGPLLU271 Lung Targeted Mutation Pre-treatment, IV 8.2 0.371 −0.284 EGFR 3.36% Cancer Analysis and WGS Day 0 E746_A750del CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.947 0.826 EGFR 0.17% Cancer Analysis and WGS Day 6 E746_A750del CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.952 −0.839 EGFR 0.00% Cancer Analysis and WGS Day 20 E746_A750del CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.944 −0.810 EGFR 0.00% Cancer Analysis and WGS Day 104 E746_A750del CGPLLU271 Lung Targeted Mutation Post-treatment, IV 8.2 0.950 −0.831 EGFR 0.44% Cancer Analysis and WGS Day 259 E746_A750del CGPLLU43 Lung Targeted Mutation Pre-treatment, IV Ongoing 0.944 −0.903 NA 0.00% Cancer Analysis and WGS Day −1 CGPLLU43 Lung Targeted Mutation Post-treatment, IV Ongoing 0.956 −0.899 NA 0.00% Cancer Analysis and WGS Day 6 CGPLLU43 Lung Targeted Mutation Post-treatment, IV Ongoing 0.959 −0.901 NA 0.00% Cancer Analysis and WGS Day 27 CGPLLU43 Lung Targeted Mutation Post-treatment, IV Ongoing 0.965 −0.896 NA 0.00% Cancer Analysis and WGS Day 83

TABLE 7 APPENDIX G: Whole genome cfDNA analyses in healthy individuals and cancer patients Correlation of Fragment Ratio Profile to Median Median Fragment cfDNA Ratio Profile Patient Stage at Fragment of Healthy Patient Type Analysis Type Timepoint Diagnosis Size (bp) Individuals CGCRC291 Colorectal Targeted Mutation Preoperative V 163 0.1972 Cancer Analysis and WGS treatment naïve CGCRC292 Colorectal Targeted Mutation Preoperative V 168 0.7804 Cancer Analysis and WGS treatment naïve CGCRC293 Colorectal Targeted Mutation Preoperative V 166 0.9335 Cancer Analysis and WGS treatment naïve CGCRC294 Colorectal Targeted Mutation Preoperative II 166 0.6531 Cancer Analysis and WGS treatment naïve CGCRC295 Colorectal Targeted Mutation Preoperative II 166 0.8161 Cancer Analysis and WGS treatment naïve CGCRC299 Colorectal Targeted Mutation Preoperative I 162 0.7325 Cancer Analysis and WGS treatment naïve CGCRC300 Colorectal Targeted Mutation Preoperative I 167 0.9382 Cancer Analysis and WGS treatment naïve CGCRC301 Colorectal Targeted Mutation Preoperative I 165 0.8252 Cancer Analysis and WGS treatment naïve CGCRC302 Colorectal Targeted Mutation Preoperative II 163 0.7499 Cancer Analysis and WGS treatment naïve CGCRC304 Colorectal Targeted Mutation Preoperative II 162 0.4642 Cancer Analysis and WGS treatment naïve CGCRC305 Colorectal Targeted Mutation Preoperative II 165 0.8909 Cancer Analysis and WGS treatment naïve CGCRG306 Colorectal Targeted Mutation Preoperative II 165 0.8523 Cancer Analysis and WGS treatment naïve CGCRC307 Colorectal Targeted Mutation Preoperative II 165 0.9140 Cancer Analysis and WGS treatment naïve CGCRC306 Colorectal Targeted Mutation Preoperative III 165 0.8734 Cancer Analysis and WGS treatment naïve CGCRC311 Colorectal Targeted Mutation Preoperative I 166 0.8535 Cancer Analysis and WGS treatment naïve CGCRC315 Colorectal Targeted Mutation Preoperative III 167 0.8083 Cancer Analysis and WGS treatment naïve CGCRC316 Colorectal Targeted Mutation Preoperative III 161 0.1546 Cancer Analysis and WGS treatment naïve CGCRC317 Colorectal Targeted Mutation Preoperative III 163 0.6242 Cancer Analysis and WGS treatment naïve CGCRC318 Colorectal Targeted Mutation Preoperative I 166 0.8824 Cancer Analysis and WGS treatment naïve CGCRC319 Colorectal Targeted Mutation Preoperative III 160 0.5979 Cancer Analysis and WGS treatment naïve CGCRC320 Colorectal Targeted Mutation Preoperative I 167 0.7949 Cancer Analysis and WGS treatment naïve CGCRC321 Colorectal Targeted Mutation Preoperative I 164 0.7604 Cancer Analysis and WGS treatment naïve CGCRC333 Colorectal Targeted Mutation Preoperative V 163 0.4263 Cancer Analysis and WGS treatment naïve CGCRC335 Colorectal Targeted Mutation Preoperative V 162 0.6466 Cancer Analysis and WGS treatment naïve CGCRC338 Colorectal Targeted Mutation Preoperative V 162 0.7740 Cancer Analysis and WGS treatment naïve CGCRC341 Colorectal Targeted Mutation Preoperative V 164 0.8995 Cancer Analysis and WGS treatment naïve CGCRC342 Colorectal Targeted Mutation Preoperative V 158 0.2524 Cancer Analysis and WGS treatment naïve CGPLBR100 Breast Targeted Mutation Preoperative III 166 0.9440 Cancer Analysis and WGS treatment naïve CGPLBR101 Breast Targeted Mutation Preoperative II 169 0.8664 Cancer Analysis and WGS treatment naïve CGPLBR102 Breast Targeted Mutation Preoperative II 169 0.9617 Cancer Analysis and WGS treatment naïve CGPLBR103 Breast Targeted Mutation Preoperative II 168 0.9498 Cancer Analysis and WGS treatment naïve CGPLBR104 Breast Targeted Mutation Preoperative II 167 0.8490 Cancer Analysis and WGS treatment naïve CGPLBR12 Breast WGS Preoperative III 164 0.8350 Cancer treatment naïve CGPLBR18 Breast WGS Preoperative III 163 0.8411 Cancer treatment naïve CGPLBR23 Breast WGS Preoperative II 166 0.9714 Cancer treatment naïve CGPLBR24 Breast WGS Preoperative II 156 0.8402 Cancer treatment naïve CGPLBR26 Breast WGS Preoperative III 165 0.9584 Cancer treatment naïve CGPLBR30 Breast WGS Preoperative II 161 0.6951 Cancer treatment naïve CGPLBR31 Breast WGS Preoperative II 167 0.9719 Cancer treatment naïve CGPLBR32 Breast WGS Preoperative II 165 0.9590 Cancer treatment naïve CGPLBR33 Breast WGS Preoperative II 166 0.9706 Cancer treatment naïve CGPLBR34 Breast WGS Preoperative II 163 0.3735 Cancer treatment naïve CGPLBR35 Breast WGS Preoperative II 168 0.9655 Cancer treatment naïve CGPLBP36 Breast WGS Preoperative II 169 0.9394 Cancer treatment naïve CGPLBR37 Breast WGS Preoperative II 167 0.9591 Cancer treatment naïve CGPLBR38 Breast Targeted Mutation Preoperative I 165 0.9105 Cancer Analysis and WGS treatment naïve CGPLBR40 Breast Targeted Mutation Preoperative III 167 0.9273 Cancer Analysis and WGS treatment naïve CGPLBR41 Breast Targeted Mutation Preoperative III 168 0.9626 Cancer Analysis and WGS treatment naïve CGPLBR45 Breast WGS Preoperative II 164 0.9615 Cancer treatment naïve CGPLBR46 Breast WGS Preoperative III 168 0.9322 Cancer treatment naïve CGPLBR47 Breast WGS Preoperative I 166 0.9461 Cancer treatment naïve CGPLBR48 Breast Targeted Mutation Preoperative II 169 0.7686 Cancer Analysis and WGS treatment naïve CGPLBR49 Breast Targeted Mutation Preoperative II 171 0.8867 Cancer Analysis and WGS treatment naïve CGPLBR50 Breast WGS Preoperative I 160 0.8593 Cancer treatment naïve CGPLBR51 Breast WGS Preoperative II 165 0.9353 Cancer treatment naïve CGPLBR52 Breast WGS Preoperative III 164 0.8688 Cancer treatment naïve CGPLBR55 Breast Targeted Mutation Preoperative III 165 0.9634 Cancer Analysis and WGS treatment naïve CGPLBR56 Breast WGS Preoperative II 163 0.9459 Cancer treatment naïve CGPLBR57 Breast Targeted Mutation Preoperative III 166 0.9672 Cancer Analysis and WGS treatment naïve CGPLBR59 Breast Targeted Mutation Preoperative I 168 0.9438 Cancer Analysis and WGS treatment naïve CGPLBR60 Breast WGS Preoperative II 167 0.9479 Cancer treatment naïve CGPLBR61 Breast Targeted Mutation Preoperative II 165 0.9611 Cancer Analysis and WGS treatment naïve CGPLBR63 Breast Targeted Mutation Preoperative II 168 0.9555 Cancer Analysis and WGS treatment naïve CGPLBR65 Breast WGS Preoperative II 167 0.9506 Cancer treatment naïve CGPLBR68 Breast Targeted Mutation Preoperative III 163 0.9154 Cancer Analysis and WGS treatment naïve CGPLBR69 Breast Targeted Mutation Preoperative II 165 0.9460 Cancer Analysis and WGS treatment naïve CGPLBR70 Breast Targeted Mutation Preoperative II 168 0.9651 Cancer Analysis and WGS treatment naïve CGPLBR71 Breast Targeted Mutation Preoperative II 165 0.9577 Cancer Analysis and WGS treatment naïve CGPLBR72 Breast Targeted Mutation Preoperative II 167 0.9786 Cancer Analysis and WGS treatment naïve CGPLBR73 Breast Targeted Mutation Preoperative II 167 0.9576 Cancer Analysis and WGS treatment naïve CGPLBR76 Breast Targeted Mutation Preoperative II 170 0.9410 Cancer Analysis and WGS treatment naïve CGPLBR81 Breast WGS Preoperative II 170 0.9043 Cancer treatment naïve CGPLBR82 Breast Targeted Mutation Preoperative I 166 0.9254 Cancer Analysis and WGS treatment naïve CGPLBR83 Breast Targeted Mutation Preoperative II 169 0.9451 Cancer Analysis and WGS treatment naïve CGPLBR84 Breast WGS Preoperative III 169 0.9315 Cancer treatment naïve CGPLBR87 Breast Targeted Mutation Preoperative II 166 0.9154 Cancer Analysis and WGS treatment naïve CGPLBR88 Breast Targeted Mutation Preoperative II 169 0.9370 Cancer Analysis and WGS treatment naïve CGPLBR90 Breast WGS Preoperative II 169 0.9002 Cancer treatment naïve CGPLBR91 Breast Targeted Mutation Preoperative III 164 0.7955 Cancer Analysis and WGS treatment naïve CGPLBR92 Breast Targeted Mutation Preoperative II 162 0.6774 Cancer Analysis and WGS treatment naïve CGPLBR93 Breast Targeted Mutation Preoperative II 164 0.8773 Cancer Analysis and WGS treatment naïve CGPLH189 Healthy WGS Preoperative NA 168 0.9325 treatment naïve CGPLH190 Healthy WGS Preoperative NA 167 0.9433 treatment naïve CGPLH192 Healthy WGS Preoperative NA 167 0.9646 treatment naïve CGPLH193 Healthy WGS Preoperative NA 167 0.5423 treatment naïve CGPLH194 Healthy WGS Preoperative NA 168 0.9567 treatment naïve CGPLH196 Healthy WGS Preoperative NA 167 0.9709 treatment naïve CGPLH197 Healthy WGS Preoperative NA 166 0.9605 treatment naïve CGPLH198 Healthy WGS Preoperative NA 167 0.9238 treatment naïve CGPLH199 Healthy WGS Preoperative NA 165 0.9618 treatment naïve CGPLH200 Healthy WGS Preoperative NA 167 0.9183 treatment naïve CGPLH201 Healthy WGS Preoperative NA 168 0.9548 treatment naïve CGPLH202 Healthy WGS Preoperative NA 168 0.9471 treatment naïve CGPLH203 Healthy WGS Preoperative NA 167 0.9534 treatment naïve CGPLH205 Healthy WGS Preoperative NA 168 0.9075 treatment naïve CGPLH208 Healthy WGS Preoperative NA 168 0.9422 treatment naïve CGPLH209 Healthy WGS Preoperative NA 169 0.9556 treatment naïve CGPLH210 Healthy WGS Preoperative NA 169 0.9447 treatment naïve CGPLH211 Healthy WGS Preoperative NA 169 0.5538 treatment naïve CGPLH300 Healthy WGS Preoperative NA 168 0.9019 treatment naïve CGPLH307 Healthy WGS Preoperative NA 168 0.9576 treatment naïve CGPLH308 Healthy WGS Preoperative NA 168 0.9481 treatment naïve CGPLH309 Healthy WGS Preoperative NA 168 0.9672 treatment naïve CGPLN310 Healthy WGS Preoperative NA 165 0.9547 treatment naïve CGPLH311 Healthy WGS Preoperative NA 167 0.9302 treatment naïve CGPLH314 Healthy WGS Preoperative NA 167 0.9482 treatment naïve CGPLH315 Healthy WGS Preoperative NA 167 0.8659 treatment naïve CGPLH316 Healthy WGS Preoperative NA 165 0.9374 treatment naïve CGPLH317 Healthy WGS Preoperative NA 169 0.9542 treatment naïve CGPLH319 Healthy WGS Preoperative NA 167 0.9578 treatment naïve CGPLR320 Healthy WGS Preoperative NA 164 0.8913 treatment naïve CGPLH322 Healthy WGS Preoperative NA 167 0.8751 treatment naïve CGPLH324 Healthy WGS Preoperative NA 169 0.9519 treatment naïve CGPLH325 Healthy WGS Preoperative NA 167 0.9124 treatment naïve CGPLH326 Healthy WGS Preoperative NA 166 0.9574 treatment naïve CGPLH327 Healthy WGS Preoperative NA 168 0.9533 treatment naïve CGPLH328 Healthy WGS Preoperative NA 166 0.9643 treatment naïve CGPLH329 Healthy WGS Preoperative NA 167 0.9609 treatment naïve CGPLH330 Healthy WGS Preoperative NA 167 0.9118 treatment naïve CGPLH331 Healthy WGS Preoperative NA 166 0.9679 treatment naïve CGPLH333 Healthy WGS Preoperative NA 169 0.9474 treatment naïve CGPLH335 Healthy WGS Preoperative NA 167 0.8909 treatment naïve CGPLH336 Healthy WGS Preoperative NA 169 0.9248 treatment naïve CGPLH337 Healthy WGS Preoperative NA 167 0.9533 treatment naïve CGPLH338 Healthy WGS Preoperative NA 165 0.9388 treatment naïve CGPLH339 Healthy WGS Preoperative NA 167 0.9396 treatment naïve CGPLH340 Healthy WGS Preoperative NA 167 0.9488 treatment naïve CGPLH341 Healthy WGS Preoperative NA 166 0.9533 treatment naïve CGPLH342 Healthy WGS Preoperative NA 166 0.7858 treatment naïve CGPLH343 Healthy WGS Preoperative NA 167 0.9421 treatment naïve CGPLH344 Healthy WGS Preoperative NA 169 0.9192 treatment naïve CGPLH345 Healthy WGS Preoperative NA 169 0.9345 treatment naïve CGPLH346 Healthy WGS Preoperative NA 169 0.9475 treatment naïve CGPLH350 Healthy WGS Preoperative NA 171 0.9570 treatment naïve CGPLH351 Healthy WGS Preoperative NA 166 0.8176 treatment naïve CGPLH352 Healthy WGS Preoperative NA 168 0.9521 treatment naïve CGPLH353 Healthy WGS Preoperative NA 167 0.9435 treatment naïve CGPLH354 Healthy WGS Preoperative NA 168 0.9481 treatment naïve CGPLH355 Healthy WGS Preoperative NA 167 0.9613 treatment naïve CGPLH356 Healthy WGS Preoperative NA 165 0.9474 treatment naïve CGPLH357 Healthy WGS Preoperative NA 167 0.9255 treatment naïve CGPLH358 Healthy WGS Preoperative NA 167 0.7777 treatment naïve CGPLH360 Healthy WGS Preoperative NA 166 0.8500 treatment naïve CGPLH361 Healthy WGS Preoperative NA 167 0.9261 treatment naïve CGPLH362 Healthy WGS Preoperative NA 167 0.9236 treatment naïve CGPLH363 Healthy WGS Preoperative NA 167 0.9488 treatment naïve CGPLH364 Healthy WGS Preoperative NA 168 0.9311 treatment naïve CGPLH365 Healthy WGS Preoperative NA 165 0.9371 treatment naïve CGPLH366 Healthy WGS Preoperative NA 167 0.9536 treatment naïve CGPLH367 Healthy WGS Preoperative NA 166 0.8748 treatment naïve CGPLH368 Healthy WGS Preoperative NA 169 0.9490 treatment naïve CGPLH369 Healthy WGS Preoperative NA 167 0.9428 treatment naïve CGPLH370 Healthy WGS Preoperative NA 167 0.9642 treatment naïve CGPLH371 Healthy WGS Preoperative NA 168 0.9621 treatment naïve CGPLH380 Healthy WGS Preoperative NA 170 0.9662 treatment naïve CGPLH381 Healthy WGS Preoperative NA 169 0.9541 treatment naïve CGPLH382 Healthy WGS Preoperative NA 167 0.9380 treatment naïve CGPLH383 Healthy WGS Preoperative NA 168 0.9700 treatment naïve CGPLH384 Healthy WGS Preoperative NA 169 0.8061 treatment naïve CGPLH385 Healthy WGS Preoperative NA 167 0.8666 treatment naïve CGPLH386 Healthy WGS Preoperative NA 167 0.6920 treatment naïve CGPLH387 Healthy WGS Preoperative NA 169 0.9583 treatment naïve CGPLH388 Healthy WGS Preoperative NA 167 0.9348 treatment naïve CGPLH389 Healthy WGS Preoperative NA 168 0.9409 treatment naïve CGPLH390 Healthy WGS Preoperative NA 167 0.9216 treatment naïve CGPLH391 Healthy WGS Preoperative NA 166 0.9334 treatment naïve CGPLH392 Healthy WGS Preoperative NA 167 0.9165 treatment naïve CGPLH393 Healthy WGS Preoperative NA 169 0.9256 treatment naïve CGPLH394 Healthy WGS Preoperative NA 167 0.9257 treatment naïve CGPLH395 Healthy WGS Preoperative NA 166 0.8611 treatment naïve CGPLH396 Healthy WGS Preoperative NA 167 0.7884 treatment naïve CGPLH398 Healthy WGS Preoperative NA 167 0.9463 treatment naïve CGPLH399 Healthy WGS Preoperative NA 169 0.8780 treatment naïve CGPLH400 Healthy WGS Preoperative NA 168 0.6662 treatment naïve CGPLH401 Healthy WGS Preoperative NA 167 0.9428 treatment naïve CGPLH402 Healthy WGS Preoperative NA 167 0.9353 treatment naïve CGPLH403 Healthy WGS Preoperative NA 168 0.9329 treatment naïve CGPLH404 Healthy WGS Preoperative NA 169 0.9402 treatment naïve CGPLH405 Healthy WGS Preoperative NA 166 0.9579 treatment naïve CGPLH406 Healthy WGS Preoperative NA 167 0.8188 treatment naïve CGPLH407 Healthy WGS Preoperative NA 169 0.9527 treatment naïve CGPLH408 Healthy WGS Preoperative NA 167 0.9584 treatment naïve CGPLH409 Healthy WGS Preoperative NA 198 0.9220 treatment naïve CGPLH410 Healthy WGS Preoperative NA 168 0.9102 treatment naïve CGPLH411 Healthy WGS Preoperative NA 167 0.9392 treatment naïve CGPLH412 Healthy WGS Preoperative NA 167 0.9561 treatment naïve CGPLH413 Healthy WGS Preoperative NA 167 0.9461 treatment naïve CGPLH414 Healthy WGS Preoperative NA 168 0.9258 treatment naïve CGPLH415 Healthy WGS Preoperative NA 169 0.9217 treatment naïve CGPLH416 Healthy WGS Preoperative NA 167 0.9672 treatment naïve CGPLH417 Healthy WGS Preoperative NA 168 0.9578 treatment naïve CGPLH418 Healthy WGS Preoperative NA 169 0.9376 treatment naïve CGPLH419 Healthy WGS Preoperative NA 167 0.9228 treatment naïve CGPLH420 Healthy WGS Preoperative NA 169 0.9164 treatment naïve CGPLH422 Healthy WGS Preoperative NA 166 0.9069 treatment naïve CGPLH423 Healthy WGS Preoperative NA 169 0.9606 treatment naïve CGPLH424 Healthy WGS Preoperative NA 167 0.9553 treatment naïve CGPLH425 Healthy WGS Preoperative NA 168 0.9722 treatment naïve CGPLH426 Healthy WGS Preoperative NA 168 0.9560 treatment naïve CGPLH427 Healthy WGS Preoperative NA 167 0.9594 treatment naïve CGPLH428 Healthy WGS Preoperative NA 167 0.9591 treatment naïve CGPLH429 Healthy WGS Preoperative NA 168 0.9358 treatment naïve CGPLH430 Healthy WGS Preoperative NA 167 0.9639 treatment naïve CGPLH431 Healthy WGS Preoperative NA 167 0.9570 treatment naïve CGPLH432 Healthy WGS Preoperative NA 168 0.9485 treatment naïve CGPLH434 Healthy WGS Preoperative NA 168 0.9571 treatment naïve CGPLH435 Healthy WGS Preoperative NA 170 0.9133 treatment naïve CGPLH436 Healthy WGS Preoperative NA 168 0.9360 treatment naïve CGPLH437 Healthy WGS Preoperative NA 170 0.9445 treatment naïve CGPLH438 Healthy WGS Preoperative NA 170 0.9537 treatment naïve CGPLM439 Healthy WGS Preoperative NA 171 0.9547 treatment naïve CGPLH440 Healthy WGS Preoperative NA 169 0.9562 treatment naïve CGPLH441 Healthy WGS Preoperative NA 167 0.9660 treatment naïve CGPLH442 Healthy WGS Preoperative NA 167 0.9569 treatment naïve CGPLH443 Healthy WGS Preoperative NA 170 0.9431 treatment naïve CGPLH444 Healthy WGS Preoperative NA 171 0.9429 treatment naïve CGPLH445 Healthy WGS Preoperative NA 171 0.9446 treatment naïve CGPLH446 Healthy WGS Preoperative NA 167 0.9502 treatment naïve CGPLH447 Healthy WGS Preoperative NA 169 0.9421 treatment naïve CGPLH448 Healthy WGS Preoperative NA 167 0.9553 treatment naïve CGPLH449 Healthy WGS Preoperative NA 167 0.9550 treatment naïve CGPLH450 Healthy WGS Preoperative NA 167 0.9572 treatment naïve CGPLH451 Healthy WGS Preoperative NA 169 0.9548 treatment naïve CGPLH452 Healthy WGS Preoperative NA 167 0.9498 treatment naïve CGPLH453 Healthy WGS Preoperative NA 166 0.9572 treatment naïve CGPLH455 Healthy WGS Preoperative NA 166 0.9526 treatment naïve CGPLH450 Healthy WGS Preoperative NA 166 0.9507 treatment naïve CGPLH457 Healthy WGS Preoperative NA 167 0.9429 treatment naïve CGPLH458 Healthy WGS Preoperative NA 167 0.9511 treatment naïve CGPLH459 Healthy WGS Preoperative NA 168 0.9609 treatment naïve CGPLH460 Healthy WGS Preoperative NA 168 0.9331 treatment naïve CGPLH463 Healthy WGS Preoperative NA 167 0.9506 treatment naïve CGPLH464 Healthy WGS Preoperative NA 170 0.9133 treatment naïve CGPLH465 Healthy WGS Preoperative NA 167 0.9251 treatment naïve CGPLH466 Healthy WGS Preoperative NA 167 0.9679 treatment naïve CGPLH467 Healthy WGS Preoperative NA 168 0.9273 treatment naïve CGPLH468 Healthy WGS Preoperative NA 167 0.8553 treatment naïve CGPLH469 Healthy WGS Preoperative NA 169 0.8225 treatment naïve CGPLH470 Healthy WGS Preoperative NA 168 0.9073 treatment naïve CGPLH471 Healthy WGS Preoperative NA 167 0.9354 treatment naïve CGPLH472 Healthy WGS Preoperative NA 166 0.8509 treatment naïve CGPLH473 Healthy WGS Preoperative NA 167 0.9206 treatment naïve CGPLH474 Healthy WGS Preoperative NA 168 0.8474 treatment naïve CGPLH475 Healthy WGS Preoperative NA 167 0.9155 treatment naïve CGPLH476 Healthy WGS Preoperative NA 169 0.8807 treatment naïve CGPLH477 Healthy WGS Preoperative NA 169 0.9129 treatment naïve CGPLH478 Healthy WGS Preoperative NA 167 0.9588 treatment naïve CGPLN479 Healthy WGS Preoperative NA 167 0.9503 treatment naïve CGPLH480 Healthy WGS Preoperative NA 169 0.9522 treatment naïve CGPLH481 Healthy WGS Preoperative NA 168 0.9568 treatment naïve CGPLH482 Healthy WGS Preoperative NA 168 0.9379 treatment naïve CGPLH483 Healthy WGS Preoperative NA 168 0.9518 treatment naïve CGPLH484 Healthy WGS Preoperative NA 166 0.9630 treatment naïve CGPLH485 Healthy WGS Preoperative NA 166 0.9547 treatment naïve CGPLH486 Healthy WGS Preoperative NA 169 0.9199 treatment naïve CGPLH487 Healthy WGS Preoperative NA 169 0.9575 treatment naïve CGPLH488 Healthy WGS Preoperative NA 167 0.9618 treatment naïve CGPLH490 Healthy WGS Preoperative NA 167 0.8950 treatment naïve CGPLH491 Healthy WGS Preoperative NA 168 0.9631 treatment naïve CGPLH492 Healthy WGS Preoperative NA 170 0.9335 treatment naïve CGPLH493 Healthy WGS Preoperative NA 168 0.8718 treatment naïve CGPLH494 Healthy WGS Preoperative NA 169 0.9623 treatment naïve CGPLH495 Healthy WGS Preoperative NA 166 0.8777 treatment naïve CGPLH496 Healthy WGS Preoperative NA 166 0.8788 treatment naïve CGPLH497 Healthy WGS Preoperative NA 167 0.9576 treatment naïve CGPLH498 Healthy WGS Preoperative NA 167 0.9526 treatment naïve CGPLH499 Healthy WGS Preoperative NA 167 0.9733 treatment naïve CGPLH500 Healthy WGS Preoperative NA 168 0.9542 treatment naïve CGPLH501 Healthy WGS Preoperative NA 169 0.9526 treatment naïve CGPLH502 Healthy WGS Preoperative NA 167 0.9512 treatment naïve CGPLH503 Healthy WGS Preoperative NA 169 0.8947 treatment naïve CGPLH504 Healthy WGS Preoperative NA 167 0.9561 treatment naïve CGPLH505 Healthy WGS Preoperative NA 166 0.9554 treatment naïve CGPLH506 Healthy WGS Preoperative NA 167 0.9733 treatment naïve CGPLH507 Healthy WGS Preoperative NA 168 0.9222 treatment naïve CGPLH508 Healthy WGS Preoperative NA 167 0.9674 treatment naïve CGPLH509 Healthy WGS Preoperative NA 167 0.9475 treatment naïve CGPLH510 Healthy WGS Preoperative NA 167 0.9459 treatment naïve CGPLH511 Healthy WGS Preoperative NA 166 0.9714 treatment naïve CGPLH512 Healthy WGS Preoperative NA 168 0.9442 treatment naïve CGPLH513 Healthy WGS Preoperative NA 166 0.9705 treatment naïve CGPLH514 Healthy WGS Preoperative NA 167 0.9690 treatment naïve CGPLH515 Healthy WGS Preoperative NA 167 0.9568 treatment naïve CGPLH516 Healthy WGS Preoperative NA 166 0.9508 treatment naïve CGPLH517 Healthy WGS Preoperative NA 168 0.9635 treatment naïve CGPLH518 Healthy WGS Preoperative NA 168 0.9647 treatment naïve CGPLH519 Healthy WGS Preoperative NA 166 0.9366 treatment naïve CGPLH520 Healthy WGS Preoperative NA 166 0.3649 treatment naïve CGPLH625 Healthy WGS Preoperative NA 166 0.8766 treatment naïve CGPLH626 Healthy WGS Preoperative NA 170 0.9011 treatment naïve CGPLH639 Healthy WGS Preoperative NA 165 0.9482 treatment naïve CGPLH640 Healthy WGS Preoperative NA 166 0.9131 treatment naïve CGPLH642 Healthy WGS Preoperative NA 167 0.9641 treatment naïve CGPLH643 Healthy WGS Preoperative NA 169 0.9450 treatment naïve CGPLH644 Healthy WGS Preoperative NA 170 0.9398 treatment naïve CGPLH646 Healthy WGS Preoperative NA 172 0.9296 treatment naïve CGPLLU144 Lung Targeted Mutation Preoperative II 164 0.8702 Cancer Analysis and WGS treatment naïve CGPLLU161 Lung Targeted Mutation Preoperative II 165 0.9128 Cancer Analysis and WGS treatment naïve CGPLLU162 Lung Targeted Mutation Preoperative II 165 0.7753 Cancer Analysis and WGS treatment naïve CGPLLU163 Lung Targeted Mutation Preoperative II 166 0.4770 Cancer Analysis and WGS treatment naïve CGPLLU168 Lung Targeted Mutation Preoperative I 163 0.9164 Cancer Analysis and WGS treatment naïve CGPLLU169 Lung Targeted Mutation Preoperative I 163 0.9326 Cancer Analysis and WGS treatment naïve CGPLLU176 Lung Targeted Mutation Preoperative I 168 0.9572 Cancer Analysis and WGS treatment naïve CGPLLU177 Lung Targeted Mutation Preoperative II 166 0.8472 Cancer Analysis and WGS treatment naïve CGPLLU203 Lung Targeted Mutation Preoperative II 164 0.9119 Cancer Analysis and WGS treatment naïve CGPLLU205 Lung Targeted Mutation Preoperative II 163 0.9518 Cancer Analysis and WGS treatment naïve CGPLLU207 Lung Targeted Mutation Preoperative II 166 0.9344 Cancer Analysis and WGS treatment naïve CGPLLU208 Lung Targeted Mutation Preoperative II 164 0.9091 Cancer Analysis and WGS treatment naïve CGPLOV11 Ovarian Targeted Mutation Preoperative V 166 0.8902 Cancer Analysis and WGS treatment naïve CGPLOV12 Ovarian Targeted Mutation Preoperative I 167 0.8779 Cancer Analysis and WGS treatment naïve CGPLOV13 Ovarian Targeted Mutation Preoperative V 166 0.7560 Cancer Analysis and WGS treatment naïve CGPLOV15 Ovarian Targeted Mutation Preoperative III 155 0.8585 Cancer Analysis and WGS treatment naïve CGPLOV18 Ovarian Targeted Mutation Preoperative III 165 0.9052 Cancer Analysis and WGS treatment naïve CGPLOV19 Ovarian Targeted Mutation Preoperative II 165 0.7854 Cancer Analysis and WGS treatment naïve CGPLOV20 Ovarian Targeted Mutation Preoperative II 165 0.8711 Cancer Analysis and WGS treatment naïve CGPLOV21 Ovarian Targeted Mutation Preoperative V 167 0.8942 Cancer Analysis and WGS treatment naïve CGPLOV22 Ovarian Targeted Mutation Preoperative III 164 0.8944 Cancer Analysis and WGS treatment naïve CGPLOV23 Ovarian Targeted Mutation Preoperative I 169 0.8510 Cancer Analysis and WGS treatment naïve CGPLOV24 Ovarian Targeted Mutation Preoperative I 166 0.9449 Cancer Analysis and WGS treatment naïve CGPLOV25 Ovarian Targeted Mutation Preoperative I 166 0.9590 Cancer Analysis and WGS treatment naïve CGPLOV26 Ovarian Targeted Mutation Preoperative I 161 0.8148 Cancer Analysis and WGS treatment naïve CGPLOV28 Ovarian Targeted Mutation Preoperative I 167 0.9635 Cancer Analysis and WGS treatment naïve CGPLOV31 Ovarian Targeted Mutation Preoperative III 167 0.9461 Cancer Analysis and WGS treatment naïve CGPLOV32 Ovarian Targeted Mutation Preoperative I 168 0.9582 Cancer Analysis and WGS treatment naïve CGPLOV37 Ovarian Targeted Mutation Preoperative I 170 0.9397 Cancer Analysis and WGS treatment naïve CGPLOV38 Ovarian Targeted Mutation Preoperative I 166 0.5779 Cancer Analysis and WGS treatment naïve CGPLOV40 Ovarian Targeted Mutation Preoperative V 170 0.6097 Cancer Analysis and WGS treatment naïve CGPLOV41 Ovarian Targeted Mutation Preoperative V 167 0.9403 Cancer Analysis and WGS treatment naïve CGPLOV42 Ovarian Targeted Mutation Preoperative I 166 0.9265 Cancer Analysis and WGS treatment naïve CGPLOV43 Ovarian Targeted Mutation Preoperative I 167 0.9626 Cancer Analysis and WGS treatment naïve CGPLOV44 Ovarian Targeted Mutation Preoperative I 164 0.9536 Cancer Analysis and WGS treatment naïve CGPLOV45 Ovarian Targeted Mutation Preoperative I 166 0.9622 Cancer Analysis and WGS treatment naïve CGPLOV47 Ovarian Targeted Mutation Preoperative I 165 0.9704 Cancer Analysis and WGS treatment naïve CGPLOV48 Ovarian Targeted Mutation Preoperative I 167 0.9675 Cancer Analysis and WGS treatment naïve CGPLOV49 Ovarian Targeted Mutation Preoperative III 164 0.8998 Cancer Analysis and WGS treatment naïve CGPLOV50 Ovarian Targeted Mutation Preoperative III 165 0.9682 Cancer Analysis and WGS treatment naïve CGPLPA112 Pancreatic WGS Preoperative II 164 0.8914 Cancer treatment naïve CGPLPA113 Duodenal WGS Preoperative I 170 0.8751 Cancer treatment naïve CGPLPA114 Bile Duct WGS Preoperative II 166 0.9098 Cancer treatment naïve CGPLPA115 Bile Duct WGS Preoperative V 165 0.8053 Cancer treatment naïve CGPLPA117 Bile Duct WGS Preoperative II 165 0.9395 Cancer treatment naïve CGPLPA118 Bile Duct Targeted Mutation Preoperative I 157 0.9406 Cancer Analysis and WGS treatment naïve CGPLPA122 Bile Duct Targeted Mutation Preoperative II 164 0.8231 Cancer Analysis and WGS treatment naïve CGPLPA124 Bile Duct Targeted Mutation Preoperative II 166 0.9108 Cancer Analysis and WGS treatment naïve CGPLPA125 Bile Duct WGS Preoperative II 165 0.9675 Cancer treatment naïve CGPLPA126 Bile Duct Targeted Mutation Preoperative II 166 0.9155 Cancer Analysis and WGS treatment naïve CGPLPA127 Bile Duct WGS Preoperative V 167 0.8916 Cancer treatment naïve CGPLPA128 Bile Duct Targeted Mutation Preoperative II 167 0.9262 Cancer Analysis and WGS treatment naïve CGPLPA129 Bile Duct Targeted Mutation Preoperative II 166 0.9220 Cancer Analysis and WGS treatment naïve CGPLPA130 Bile Duct Targeted Mutation Preoperative II 169 0.8586 Cancer Analysis and WGS treatment naïve CGPLPA131 Bile Duct Targeted Mutation Preoperative II 165 0.7707 Cancer Analysis and WGS treatment naïve CGPLPA134 Bile Duct Targeted Mutation Preoperative II 160 0.7502 Cancer Analysis and WGS treatment naïve CGPLPA135 Bile Duct WGS Preoperative I 165 0.9495 Cancer treatment naïve CGPLPA136 Bile Duct Targeted Mutation Preoperative II 164 0.9289 Cancer Analysis and WGS treatment naïve CGPLPA137 Bile Duct WGS Preoperative II 166 0.9568 Cancer treatment naïve CGPLPA139 Bile Duct WGS Preoperative V 166 0.9511 Cancer treatment naïve CGPLPA14 Pancreatic WGS Preoperative II 167 0.8718 Cancer treatment naïve CGPLPA140 Bile Duct Targeted Mutation Preoperative II 166 0.9215 Cancer Analysis and WGS treatment naïve CGPLPA141 Bile Duct WGS Preoperative II 165 0.3172 Cancer treatment naïve CGPLPA15 Pancreatic WGS Preoperative II 167 0.9111 Cancer treatment naïve CGPLPA155 Bile Duct WGS Preoperative II 165 0.9496 Cancer treatment naïve CGPLPA156 Pancreatic WGS Preoperative II 167 0.9479 Cancer treatment naïve CGPLPA165 Bile Duct WGS Preoperative I 168 0.9596 Cancer treatment naïve CGPLPA168 Bile Duct WGS Preoperative II 162 0.7838 Cancer treatment naïve CGPLPA17 Pancreatic WGS Preoperative II 166 0.8624 Center treatment naïve CGPLPA184 Bile Duct WGS Preoperative II 165 0.9100 Cancer treatment naïve CGPLPA187 Bile Duct WGS Preoperative II 165 0.8577 Cancer treatment naïve CGPLPA23 Pancreatic WGS Preoperative II 165 0.7887 Cancer treatment naïve CGPLPA25 Pancreatic WGS Preoperative II 166 0.9549 Cancer treatment naïve CGPLPA26 Pancreatic WGS Preoperative II 166 0.9598 Cancer treatment naïve CGPLPA28 Pancreatic WGS Preoperative II 165 0.9069 Cancer treatment naïve CGPLPA33 Pancreatic WGS Preoperative II 166 0.8361 Cancer treatment naïve CGPLPA34 Pancreatic WGS Preoperative II 168 0.9846 Cancer treatment naïve CGPLPA37 Pancreatic WGS Preoperative II 165 0.8840 Cancer treatment naïve CGPLPA38 Pancreatic WGS Preoperative II 167 0.8746 Cancer treatment naïve CGPLPA39 Pancreatic WGS Preoperative II 167 0.8562 Cancer treatment naïve CGPLPA40 Pancreatic WGS Preoperative II 165 0.8563 Cancer treatment naïve CGPLPA42 Pancreatic WGS Preoperative II 167 0.9126 Cancer treatment naïve CGPLPA46 Pancreatic WGS Preoperative II 169 0.8274 Cancer treatment naïve CGPLPA47 Pancreatic WGS Preoperative II 166 0.8376 Cancer treatment naïve CGPLPA48 Pancreatic WGS Preoperative I 167 0.9391 Cancer treatment naïve CGPLPA52 Pancreatic WGS Preoperative II 167 0.9452 Cancer treatment naïve CGPLPA53 Pancreatic WGS Preoperative I 163 0.9175 Cancer treatment naïve CGPLPA58 Pancreatic WGS Preoperative II 165 0.9587 Cancer treatment naïve CGPLPA59 Pancreatic WGS Preoperative II 163 0.9230 Cancer treatment naïve CGPLPA67 Pancreatic WGS Preoperative II 165 0.9574 Cancer treatment naïve CGPLPA69 Pancreatic WGS Preoperative I 168 0.9172 Cancer treatment naïve CGPLPA71 Pancreatic WGS Preoperative II 167 0.9424 Cancer treatment naïve CGPLPA74 Pancreatic WGS Preoperative II 165 0.9688 Cancer treatment naïve CGPLPA78 Pancreatic WGS Preoperative II 163 0.9681 Cancer treatment naïve CGPLPA85 Pancreatic WGS Preoperative II 165 0.9137 Cancer treatment naïve CGPLPA86 Pancreatic WGS Preoperative II 165 0.8875 Cancer treatment naïve CGPLPA92 Pancreatic WGS Preoperative II 167 0.9389 Cancer treatment naïve CGPLPA93 Pancreatic WGS Preoperative II 166 0.8585 Cancer treatment naïve CGPLPA94 Pancreatic WGS Preoperative II 162 0.9365 Cancer treatment naïve CGPLPA95 Pancreatic WGS Preoperative II 163 0.8542 Cancer treatment naïve CGST102 Gastric Targeted Mutation Preoperative II 167 0.9496 cancer Analysis and WGS treatment naïve CGST11 Gastric WGS Preoperative IV 169 0.9419 cancer treatment naïve CGST110 Gastric Targeted Mutation Preoperative II 167 0.9626 cancer Analysis and WGS treatment naïve CGST114 Gastric Targeted Mutation Preoperative II 164 0.9535 cancer Analysis and WGS treatment naïve CGST13 Gastric Targeted Mutation Preoperative II 166 0.9369 cancer Analysis and WGS treatment naïve CGST131 Gastric WGS Preoperative II 171 0.9428 cancer treatment naïve CGST141 Gastric Targeted Mutation Preoperative II 168 0.9621 cancer Analysis and WGS treatment naïve CGST16 Gastric Targeted Mutation Preoperative II 166 0.7804 cancer Analysis and WGS treatment naïve CGST18 Gastric Targeted Mutation Preoperative II 169 0.9523 cancer Analysis and WGS treatment naïve CGST21 Gastric WGS Preoperative II 165 −0.4778 cancer treatment naïve CGST26 Gastric WGS Preoperative IV 166 0.9554 cancer treatment naïve CG3T28 Gastric Targeted Mutation Preoperative X 169 0.9076 cancer Analysis and WGS treatment naïve CGST30 Gastric Targeted Mutation Preoperative II 169 0.9246 cancer Analysis and WGS treatment naïve CGST32 Gastric Targeted Mutation Preoperative II 169 0.9431 cancer Analysis and WGS treatment naïve CGST33 Gastric Targeted Mutation Preoperative I 168 0.7999 cancer Analysis and WGS treatment naïve CGST38 Gastric WGS Preoperative 0 168 0.9368 cancer treatment naïve CGST39 Gastric Targeted Mutation Preoperative IV 164 0.8742 cancer Analysis and WGS treatment naïve CGST41 Gastric Targeted Mutation Preoperative IV 168 0.8194 cancer Analysis and WGS treatment naïve CGST45 Gastric Targeted Mutation Preoperative II 168 0.9576 cancer Analysis and WGS treatment naïve CGST47 Gastric Targeted Mutation Preoperative I 168 0.9611 cancer Analysis and WGS treatment naïve CGST48 Gastric Targeted Mutation Preoperative IV 167 0.7469 cancer Analysis and WGS treatment naïve CGST53 Gastric WGS Preoperative 0 173 0.0019 cancer treatment naïve CGST58 Gastric Targeted Mutation Preoperative II 169 0.9470 cancer Analysis and WGS treatment naïve CGST67 Gastric WGS Preoperative I 170 0.9352 cancer treatment naïve CGST77 Gastric WGS Preoperative IV 170 0.00438 cancer treatment naïve CGST80 Gastric Targeted Mutation Preoperative II 168 0.9313 cancer Analysis and WGS treatment naïve CGST81 Gastric Targeted Mutation Preoperative I 168 0.9480 cancer Analysis and WGS treatment naïve Correlation of GC Corrected Fragment Mutant Ratio Profile Alelle to Median Fraction Fraction Fragment of Reads Detected Detected Detected Ratio Profile Mapped to using using using of Healthy Mitochondrial DELFI DELFI (95% DELFI (98% Targeted Patient Individuals Genome Scene specificity) specificity) sequencing* CGCRC291 0.5268 0.0484% 0.9976 Y Y 22.85%  CGCRC232 0.8835 0.0270% 0.7299 Y N 1.41% CGCRC293 0.9206 0.0748% 0.5534 N N 0.35% CGCRC294 0.8904 0.0188% 0.8757 Y Y 0.17% CGCRC295 0.8895 0.0369% 0.9951 Y Y ND CGCRC299 0.9268 0.0392% 0.9648 Y Y ND CGCRC300 0.0303 0.0235% 0.4447 N N ND CGCRC301 0.9151 0.0310% 0.2190 N N 0.21% CGCRC302 0.9243 0.0112% 0.9897 Y Y 0.12% CGCRC304 0.9360 0.0393% 0.9358 Y Y 0.27% CGCRC305 0.9250 0.0120% 0.3988 Y Y 0.19% CGCRG306 0.8186 0.0781% 0.9486 Y Y 8.02% CGCRC307 0.9342 0.0181% 0.7042 Y N 0.58% CGCRC306 0.9324 0.0078% 0.9082 Y Y 0.11% CGCRC311 0.9156 0.0173% 0.1887 N N ND CGCRC315 0.8846 0.0241% 0.6422 Y N 0.27% CGCRC316 0.5879 0.0315% 0.9971 Y Y 5.52% CGCRC317 0.8944 0.0184% 0.9855 Y Y 0.36% CGCRC318 0.9140 0.0156% 0.5615 N N ND CGCRC319 0.8230 0.1259% 0.9925 Y Y 3.11% CGCRC320 0.9101 0.0383% 0.8019 Y Y 0.84% CGCRC321 0.9021 0.0829% 0.9759 Y Y 0.20% CGCRC333 0.4355 0.4284% 0.9974 Y Y 43.03%  CGCRC335 0.6856 0.1154% 0.9887 Y Y 81.61%  CGCRC338 0.7573 0.1436% 0.9976 Y Y 36.00%  CGCRC341 0.9191 0.0197% 0.9670 Y Y ND CGCRC342 0.1345 0.1732% 0.9987 Y Y 30.72%  CGPLBR100 0.8945 0.1234% 0.8684 Y Y ND CGPLBR101 0.9304 0.0709% 0.9385 Y Y ND CGPLBR102 0.9345 0.4742% 0.9052 Y Y 0.25% CGPLBR103 0.9251 0.0775% 0.5994 N N ND CGPLBR104 0.9192 0.0532% 0.9950 Y Y 0.13% CGPLBR12 0.7760 0.1407% 0.7598 Y Y — CGPLBR18 0.9534 0.0267% 0.3886 N N — CGPLBR23 0.9312 0.0144% 0.1235 N N — CGPLBR24 0.8766 0.0210% 0.7480 Y Y — CGPLBR26 0.9120 0.1456% 0.9630 Y Y — CGPLBR30 0.6611 0.0952% 0.9956 Y Y — CGPLBR31 0.9556 0.0427% 0.2227 N N — CGPLBR32 0.9229 0.0306% 0.9815 Y Y — CGPLBR33 0.9432 0.0817% 0.2853 N N — CGPLBR34 0.9425 0.0115% 0.1637 N N — CGPLBR35 0.9348 0.1371% 0.5057 N N — CGPLBR36 0.8884 0.0813% 0.4017 N N — CGPLBR37 0.9495 0.0516% 0.0314 N N — CGPLBR38 0.0349 0.1352% 0.8983 Y Y 0.53% CGPLBR40 0.9244 0.0923% 0.9846 Y Y ND CGPLBR41 0.9346 0.0544% 0.9416 Y Y 0.32% CGPLBR45 0.9286 0.0296% 0.3860 N N CGPLBR46 0.9005 0.0345% 0.7270 Y N — CGPLBR47 0.2028 0.0591% 0.8247 Y Y — CGPLBR48 0.8246 0.0504% 0.9973 Y Y 0.18% CGPLBR49 0.7887 0.0377% 0.9946 Y Y ND CGPLBR50 0.8332 0.0137% 0.6820 Y N — CGPLBR51 0.9160 0.0863% 0.6915 Y N — CGPLBR52 0.9196 0.0165% 0.6390 Y N — CGPLBR55 0.9341 0.0356% 0.9494 Y Y 0.68% CGPLBR56 0.9428 0.2025% 0.4700 N N — CGPLBR57 0.9416 0.0902% 0.9090 Y Y ND CGPLBR59 0.9130 0.0761% 0.5828 N N ND CGPLBR60 0.8916 0.0626% 0.8779 Y Y — CGPLBR61 0.9422 0.0601% 0.4417 N N 0.44% CGPLBR63 0.9132 0.0514% 0.8788 Y Y ND CGPLBR65 0.8970 0.0264% 0.9048 Y Y — CGPLBR68 0.9532 0.0164% 0.7863 Y Y ND CGPLBR69 0.9474 0.0279% 0.0600 N N ND CGPLBR70 0.9388 0.0171% 0.6447 Y N 0.36% CGPLBR71 0.9368 0.0271% 0.6706 Y N 0.10% CGPLBR72 0.9640 0.0263% 0.6129 N N ND CGPLBR73 0.9421 0.0142% 0.0746 N N 0.27% CGPLBR76 0.9254 0.0775% 0.9334 Y Y 3.12% CGPLBR81 0.8193 0.0241% 0.9899 Y Y — CGPLBR82 0.9288 0.1640% 0.9834 Y Y 0.12% CGPLBR83 0.9138 0.0419% 0.9810 Y Y 0.28% CGPLBR84 0.8359 0.0274% 0.9901 Y Y — CGPLBR87 0.8797 0.0294% 0.9988 Y Y 0.45% CGPLBR88 0.8547 0.0181% 0.9988 Y Y 0.38% CGPLBR90 0.8330 0.0417% 0.9687 Y Y CGPLBR91 0.9408 0.0799% 0.8710 Y Y ND CGPLBR92 0.8835 0.1042% 0.9866 Y Y 0.20% CGPLBP93 0.9072 0.0352% 0.7253 Y N ND CGPLH189 0.8947 0.0591% 0.1748 N N — CGPLH190 0.9369 0.1193% 0.5188 N N — CGPLH192 0.9487 0.0276% 0.0178 N N — CGPLH193 0.9442 0.0420% 0.5794 N N — CGPLH194 0.9289 0.0407% 0.1616 N N — CGPLH196 0.9512 0.0266% 0.0999 N N — CGPLH197 0.9416 0.0334% 0.4699 N N — CGPLH198 0.9457 0.0302% 0.6571 Y N CGPLH199 0.9439 0.0170% 0.5564 N N — CGPLH200 0.9391 0.0362% 0.3833 N N — CGPLH201 0.9180 0.0470% 0.8395 Y Y — CGPLH202 0.9436 0.0501% 0.1088 N N — CGPLH203 0.9575 0.0455% 0.2485 N N — CGPLH205 0.9283 0.0409% 0.4401 N N — CGPLH208 0.9409 0.0371% 0.2706 N N — CGPLH209 0.9367 0.0427% 0.2213 N N — CGPLH210 0.9181 0.0279% 0.3500 N N — CGPLH211 0.9410 0.0317% 0.1752 N N — CGPLH300 0.9200 0.0397% 0.0226 N N CGPLH307 0.9167 0.0388% 0.1789 N N — CGPLH308 0.9352 0.0311% 0.0185 N N — CGPLH309 0.9451 0.0226% 0.0441 N N — CGPLN310 0.9527 0.0145% 0.7135 Y N — CGPLH311 0.9348 0.0202% 0.2589 N N — CGPLH314 0.9491 0.0212% 0.1632 N N — CGPLH315 0.9427 0.0071% 0.3450 N N — CGPLH316 0.9552 0.0191% 0.4697 N N — CGPLH317 0.9352 0.0232% 0.1330 N N — CGPLH319 0.9189 0.0263% 0.2232 N N — CGPLR320 0.9166 0.0222% 0.1095 N N — CGPLH322 0.9411 0.0248% 0.0749 N N — CGPLH324 0.9133 0.0402% 0.0128 N N — CGPLH325 0.9202 0.0711% 0.0102 N N — CGPLH326 0.9408 0.0213% 0.0475 N N — CGPLH327 0.9071 0.1275% 0.4891 N N — CGPLH328 0.9332 0.0256% 0.0234 N N — CGPLH329 0.9396 0.0269% 0.0139 N N — CGPLH330 0.9403 0.0203% 0.2642 N N — CGPLH331 0.9377 0.0314% 0.0304 N N — CGPLH333 0.9132 0.0350% 0.1633 N N — CGPLH335 0.9333 0.0285% 0.0096 N N — CGPLH336 0.9159 0.0159% 0.3872 N N — CGPLH337 0.9262 0.0367% 0.2976 N N — CGPLH338 0.9303 0.0103% 0.0431 N N — CGPLH339 0.9338 0.0280% 0.0379 N N — CGPLH340 0.9321 0.0210% 0.0379 N N — CGPLH341 0.9187 0.0448% 0.1775 N N — CGPLH342 0.8986 0.0283% 0.0904 N N — CGPLH343 0.9067 0.0632% 0.0160 N N — CGPLH344 0.8998 0.0257% 0.0120 N N — CGPLH345 0.9107 0.0445% 0.0031 N N — CGPLH346 0.9074 0.0208% 0.0686 N N — CGPLH350 0.9288 0.0284% 0.0071 N N — CGPLH351 0.9294 0.0223% 0.0207 N N — CGPLH352 0.9190 0.0613% 0.0512 N N — CGPLH353 0.9130 0.0408% 0.0132 N N — CGPLH354 0.9121 0.0318% 0.0082 N N — CGPLH355 0.9308 0.0400% 0.6407 Y N — CGPLH356 0.9312 0.0427% 0.2437 N N — CGPLH357 0.9340 0.0217% 0.0070 N N CGPLH358 0.9372 0.0174% 0.1451 N N — CGPLH360 0.8775 0.3395% 0.0048 N N — CGPLH361 0.9283 0.0266% 0.1524 N N — CGPLH362 0.9503 0.0309% 0.4832 N N — CGPLH363 0.9187 0.0620% 0.0199 N N — CGPLH364 0.9480 0.0282% 0.8719 Y Y — CGPLH365 0.9051 0.1740% 0.9683 Y Y — CGPLH366 0.9170 0.0344% 0.0952 N N — CGPLH367 0.9181 0.0353% 0.1235 N N — CGPLH368 0.9076 0.1073% 0.1252 N N — CGPLH369 0.9541 0.0246% 0.2821 N N — CGPLH370 0.9423 0.0410% 0.0989 N N — CGPLH371 0.9414 0.0734% 0.2173 N N — CGPLH380 0.9424 0.0523% 0.0128 N N — CGPLH381 0.9501 0.0435% 0.0152 N N — CGPLH382 0.9584 0.0340% 0.0326 N N — CGPLH383 0.9407 0.0389% 0.0035 N N — CGPLH384 0.9043 0.0207% 0.0258 N N — CGPLH385 0.9245 0.0165% 0.0566 N N — CGPLH386 0.8859 0.0502% 0.2677 N N CGPLH387 0.9223 0.0375% 0.0081 N N — CGPLH388 0.9266 0.0527% 0.0499 N N — CGPLH389 0.9035 0.0667% 0.6585 Y N — CGPLH390 0.9182 0.0229% 0.0837 N N — CGPLH391 0.9162 0.0223% 0.0716 N N — CGPLH392 0.9014 0.0424% 0.1305 N N — CGPLH393 0.9045 0.0407% 0.0037 N N — CGPLH394 0.9292 0.6522% 0.1073 N N — CGPLH395 0.9254 0.0424% 0.0171 N N — CGPLH396 0.8928 0.0393% 0.0303 N N — CGPLH398 0.9578 0.0242% 0.3195 N N CGPLH399 0.9195 0.0573% 0.0685 N N — CGPLH400 0.9047 0.0300% 0.2103 N N — CGPLH401 0.9339 0.0146% 0.0620 N N — CGPLH402 0.8800 0.1516% 0.0395 N N — CGPLH403 0.8829 0.0515% 0.0223 N N — CGPLH404 0.8948 0.0528% 0.0027 N N — CGPLH405 0.9204 0.0359% 0.0188 N N — CGPLH406 0.8592 0.0667% 0.0206 N N — CGPLH407 0.9099 0.0229% 0.0040 N N — CGPLH408 0.9192 0.0415% 0.1257 N N — CGPLH409 0.8950 0.0302% 0.0056 N N — CGPLH410 0.9006 0.0453% 0.0019 N N — CGPLH411 0.8857 0.0621% 0.0188 N N — CGPLH412 0.9191 0.0140% 0.0417 N N — CGPLH413 0.9145 0.0355% 0.0084 N N — CGPLH414 0.9127 0.0290% 0.0284 N N — CGPLH415 0.9025 0.0296% 0.0131 N N — CGPLH416 0.9388 0.0198% 0.0645 N N — CGPLH417 0.9192 0.0241% 0.0836 N N — CGPLH418 0.9234 0.0306% 0.0052 N N — CGPLH419 0.9295 0.0280% 0.0469 N N — CGPLH420 0.9108 0.0187% 0.0420 N N — CGPLH422 0.9006 0.0209% 0.0324 N N — CGPLH423 0.9289 0.0832% 0.0139 N N — CGPLH424 0.9265 0.1119% 0.0864 N N — CGPLH425 0.9488 0.0722% 0.0156 N N — CGPLH426 0.9080 0.0548% 0.1075 N N — CGPLH427 0.9257 0.0182% 0.0470 N N — CGPLH428 0.9272 0.0346% 0.0182 N N — CGPLH429 0.8757 0.0593% 0.8143 Y Y — CGPLH430 0.9307 0.0258% 0.0369 N N — CGPLH431 0.9185 0.0234% 0.0174 N N — CGPLH432 0.9082 0.0433% 0.0181 N N — CGPLH434 0.9442 0.0297% 0.0050 N N — CGPLH435 0.9097 0.0179% 0.0441 N N — CGPLH436 0.9158 0.0290% 0.0958 N N — CGPLH437 0.3245 0.0156% 0.0136 N N — CGPLH438 0.9138 0.0169% 0.1041 N N — CGPLM439 0.9028 0.0225% 0.0078 N N — CGPLH440 0.9295 0.0330% 0.0887 N N — CGPLH441 0.9430 0.0178% 0.0085 N N CGPLH442 0.9406 0.0169% 0.0582 N N — CGPLH443 0.8801 0.0207% 0.0578 N N — CGPLH444 0.9066 0.6464% 0.0097 N N — CGPLH445 0.8750 0.0267% 0.1939 N N — CGPLH446 0.9257 0.0281% 0.0340 N N — CGPLH447 0.8968 0.0167% 0.0017 N N — CGPLH448 0.8181 0.0401% 0.0389 N N — CGPLH449 0.9254 0.0236% 0.0116 N N — CGPLH450 0.9195 0.0331% 0.0597 N N — CGPLH451 0.9167 0.0262% 0.0104 N N — CGPLH452 0.8948 0.0480% 0.4722 N N — CGPLH453 0.9339 0.0186% 0.3419 N N — CGPLH455 0.9322 0.0455% 0.4536 N N — CGPLH450 0.9098 0.0207% 0.0365 N N — CGPLH457 0.9022 0.0298% 0.0354 N N — CGPLH458 0.9275 0.0298% 0.1891 N N — CGPLH459 0.9209 0.0281% 0.0371 N N — CGPLH460 0.8863 0.0227% 0.1157 N N — CGPLH463 0.9372 0.0130% 0.0865 N N — CGPLH464 0.8511 0.0659% 0.2040 N N CGPLH465 0.9164 0.0325% 0.0121 N N — CGPLH466 0.9408 0.0155% 0.1733 N N — CGPLH467 0.9024 0.0229% 0.2303 N N — CGPLH468 0.9345 0.0247% 0.5427 N N — CGPLH469 0.8799 0.0201% 0.5351 N N — CGPLH470 0.2228 0.0715% 0.0327 N N — CGPLH471 0.9333 0.0153% 0.0406 N N — CGPLH472 0.8915 0.0481% 0.6152 N N — CGPLH473 0.9128 0.0443% 0.2995 N N — CGPLH474 0.9245 0.0316% 0.5246 Y N — CGPLH475 0.9233 0.0269% 0.0736 N N CGPLH476 0.9059 0.0236% 0.0143 N N — CGPLH477 0.9376 0.0382% 0.1111 N N — CGPLH478 0.9344 0.0256% 0.0828 N N — CGPLN479 0.9207 0.0221% 0.0648 N N — CGPLH480 0.9046 0.0672% 0.7473 Y N — CGPLH481 0.9113 0.0311% 0.0282 N N — CGPLH482 0.9336 0.0162% 0.0058 N N — CGPLH483 0.9275 0.0251% 0.0495 N N — CGPLH484 0.9366 0.0261% 0.0048 N N — CGPLH485 0.9128 0.0291% 0.1084 N N — CGPLH486 0.9042 0.0220% 0.0820 N N — CGPLH487 0.9098 0.0594% 0.2154 N N — CGPLH488 0.9298 0.0409% 0.0903 N N — CGPLH490 0.8794 0.0432% 0.0424 N N — CGPLH491 0.9332 0.0144% 0.0223 N N — CGPLH492 0.8799 0.0322% 0.0311 N N — CGPLH493 0.9330 0.0065% 0.0280 N N — CGPLH494 0.9303 0.0232% 0.0824 N N — CGPLH495 0.8908 0.0513% 0.0465 N N — CGPLH496 0.9398 0.0208% 0.0572 N N — CGPLH497 0.9330 0.0335% 0.0404 N N — CGPLH498 0.9315 0.0403% 0.0752 N N — CGPLH499 0.9442 0.0198% 0.0149 N N — CGPLH500 0.9240 0.0433% 0.0754 N N — CGPLH501 0.9308 0.0300% 0.0159 N N — CGPLH502 0.9200 0.0351% 0.0841 N N — CGPLH503 0.8939 0.0398% 0.0649 N N — CGPLH504 0.9324 0.0440% 0.1231 N N — CGPLH505 0.9243 0.0605% 0.1889 N N — CGPLH506 0.9498 0.0284% 0.0180 N N — CGPLH507 0.9192 0.0186% 0.0848 N N — CGPLH508 0.9410 0.0150% 0.1077 N N — CGPLH509 0.9323 0.0163% 0.0828 N N — CGPLH510 0.9548 0.0128% 0.0378 N N — CGPLH511 0.9493 0.0224% 0.1779 N N — CGPLH512 0.9244 0.0094% 0.0076 N N — CGPLH513 0.9595 0.0441% 0.5250 N N — CGPLH514 0.9369 0.0114% 0.3131 N N — CGPLH515 0.9283 0.0352% 0.4936 N N — CGPLH516 0.9298 0.0175% 0.0916 N N — CGPLH517 0.9494 0.0161% 0.0059 N N CGPLH518 0.9432 0.0274% 0.0130 N N — CGPLH519 0.9351 0.0171% 0.0949 N N — CGPLH520 0.9476 0.0241% 0.0944 N N — CGPLH625 0.9231 0.0697% 0.4977 N N — CGPLH626 0.9269 0.0231% 0.3100 N N — CGPLH639 0.9410 0.0549% 0.0773 N N — CGPLH640 0.9264 0.0232% 0.0327 N N — CGPLH642 0.9376 0.0768% 0.0555 N N — CGPLH643 0.9271 0.0579% 0.1325 N N — CGPLH644 0.8948 0.0621% 0.3819 N N — CGPLH646 0.8691 0.0462% 0.2423 N N — CGPLLU144 0.8681 0.0423% 0.9892 Y Y 5.10% CGPLLU161 0.9187 0.0273% 0.9955 Y Y 0.20% CGPLLU162 0.8836 0.1410% 0.9986 Y Y 0.22% CGPLLU163 0.3033 0.0724% 0.9940 Y Y 0.21% CGPLLU168 0.8842 0.0712% 0.9861 Y Y 0.07% CGPLLU169 0.9189 0.0846% 0.9866 Y Y 0.13% CGPLLU176 0.9081 0.0626% 0.8769 Y Y ND CGPLLU177 0.6790 0.0564% 0.9924 Y Y 3.22% CGPLLU203 0.8741 0.0568% 0.9178 Y Y 0.11% CGPLLU205 0.9476 0.0495% 0.9677 Y Y ND CGPLLU207 0.9379 0.0421% 0.9908 Y Y 0.32% CGPLLU208 0.8342 0.0815% 0.9273 Y Y 1.33% CGPLOV11 0.8872 0.0463% 0.9343 Y Y 0.87% CGPLOV12 0.8973 0.2767% 0.9764 Y Y ND CGPLOV13 0.9146 0.1017% 0.9690 Y Y 0.35% CGPLOV15 0.8552 0.0876% 0.9945 Y Y 3.54% CGPLOV18 0.9046 0.0400% 0.9983 Y Y 1.12% CGPLOV19 0.7578 0.1089% 0.9989 Y Y 46.35%  CGPLOV20 0.9154 0.0581% 0.9749 Y Y 0.21% CGPLOV21 0.8889 0.0677% 0.9951 Y Y 14.36%  CGPLOV22 0.9355 0.0251% 0.9775 Y V 0.49% CGPLOV23 0.8850 0.1520% 0.9916 Y Y 1.39% CGPLOV24 0.8995 0.0303% 0.9856 Y Y ND CGPLOV25 0.9228 0.0141% 0.8544 Y Y ND CGPLOV26 0.9351 0.0646% 0.9946 Y Y ND CGPLOV28 0.9378 0.0647% 0.8160 Y Y ND CGPLOV31 0.9293 0.1605% 0.9795 Y Y ND CGPLOV32 0.9338 0.1351% 0.8609 Y Y ND CGPLOV37 0.8831 0.0986% 0.9849 Y Y 0.29% CGPLOV38 0.6502 0.0490% 0.9990 Y Y 4.89% CGPLOV40 0.8127 0.6145% 0.9983 Y Y 6.73% CGPLOV41 0.8929 0.1110% 0.9484 Y Y 0.60% CGPLOV42 0.9086 0.0489% 0.9979 Y Y 1.24% CGPLOV43 0.9342 0.0432% 0.6042 N N ND CGPLOV44 0.9173 0.1946% 0.9962 Y Y 0.37% CGPLOV45 0.9291 0.0801% 0.9128 Y Y ND CGPLOV47 0.9461 0.0270% 0.3410 N N 3.20% CGPLOV48 0.9429 0.0422% 0.4874 N N 10.70%  CGPLOV49 0.8083 0.1527% 0.9897 Y Y 2.03% CGPLOV50 0.9382 0.0807% 0.9955 Y Y ND CGPLPA112 0.0429 0.0268% 0.0856 N N — CGPLPA113 0.7674 1.0116% 0.9935 Y Y — CGPLPA114 0.9246 0.0836% 0.7598 Y Y — CGPLPA115 0.8310 0.0763% 0.9974 Y Y — CGPLPA117 0.8767 0.1084% 0.9049 Y Y — CGPLPA118 0.9001 0.1842% 0.9859 Y Y 0.14% CGPLPA122 0.8058 0.2047% 0.9983 Y Y 37.22%  CGPLPA124 0.9238 0.1542% 0.8791 Y Y 0.62% CGPLPA125 0.9373 0.0273% 0.0228 N N — CGPLPA126 0.9139 0.4349% 0.9908 Y Y ND CGPLPA127 0.8117 0.4371% 0.9789 Y Y — CGPLPA128 0.9003 0.1317% 0.9812 Y Y ND CGPLPA129 0.9155 0.0612% 0.9839 Y Y ND CGPLPA130 0.8499 0.1005% 0.9895 Y Y ND CGPLPA131 0.9195 0.0780% 0.9885 Y Y 3.21% CGPLPA134 0.8847 0.0260% 0.9896 Y Y 0.93% CGPLPA135 0.9184 0.0558% 0.6594 Y N — CGPLPA136 0.9050 0.0769% 0.9596 Y Y 0.10% CGPLPA137 0.9320 0.0499% 0.7282 Y N — CGPLPA139 0.9374 0.0465% 0.0743 N N — CGPLPA14 0.9069 0.0515% 0.9824 Y Y CGPLPA140 0.9548 0.0330% 0.9761 Y Y 0.21% CGPLPA141 0.9381 0.0920% 0.9988 Y Y — CGPLPA15 0.8927 0.0160% 0.8737 Y Y — CGPLPA155 0.9313 0.0260% 0.8013 Y Y — CGPLPA156 0.9432 0.0290% 0.0159 N N — CGPLPA165 0.9309 0.0555% 0.2158 N N — CGPLPA168 0.7757 0.3123% 0.9878 Y Y — CGPLPA17 0.6771 1.2600% 0.9956 Y Y — CGPLPA184 0.9203 0.0897% 0.9926 Y Y CGPLPA187 0.8968 0.0658% 0.9675 Y Y — CGPLPA23 0.6938 0.5785% 0.9984 Y Y — CGPLPA25 0.9239 0.0380% 0.8103 Y Y — CGPLPA26 0.9356 0.0247% 0.8231 Y Y — CGPLPA28 0.8938 0.0546% 0.9036 Y Y — CGPLPA33 0.8553 0.0894% 0.9367 Y Y — CGPLPA34 0.8885 0.0439% 0.7977 Y Y — CGPLPA37 0.9294 0.0410% 0.9924 Y Y — CGPLPA38 0.8941 0.0372% 0.9851 Y Y — CGPLPA39 0.7972 0.5058% 0.9951 Y Y — CGPLPA40 0.8865 0.2268% 0.9920 Y Y CGPLPA42 0.8363 0.0283% 0.3544 N N — CGPLPA46 0.7525 1.0982% 0.9952 Y Y — CGPLPA47 0.8439 0.1596% 0.9346 Y Y — CGPLPA48 0.9207 1.0232% 0.2251 N N — CGPLPA52 0.8863 0.0154% 0.0963 N N — CGPLPA53 0.8776 0.1824% 0.8946 Y Y — CGPLPA58 0.9224 0.0803% 0.9056 Y Y — CGPLPA59 0.9193 0.1479% 0.9759 Y Y — CGPLPA67 0.9248 0.0329% 0.6716 Y N — CGPLPA69 0.8592 0.0459% 0.1245 N N — CGPLPA71 0.8888 0.0479% 0.0524 N N — CGPLPA74 0.9372 0.0292% 0.0108 N N — CGPLPA78 0.9441 0.0345% 0.0897 N N — CGPLPA85 0.9337 0.0363% 0.0508 N N — CGPLPA86 0.8042 0.7564% 0.9864 Y Y — CGPLPA92 0.9003 0.1459% 0.7061 Y N — CGPLPA93 0.8023 0.6250% 0.9978 Y Y — CGPLPA94 0.9433 0.0160% 0.9025 Y Y — CGPLPA95 0.8571 0.0815% 0.9941 Y Y CGST102 0.9057 0.0704% 0.8581 Y Y 0.43% CGST11 0.9161 0.0651% 0.1435 N N — CGST110 0.9232 0.0817% 0.8900 Y Y ND CGST114 0.9038 0.0317% 0.5593 N N ND CGST13 0.9156 0.0321% 0.9754 Y Y ND CGST131 0.8886 0.2752% 0.9409 Y Y — CGST141 0.9206 0.0338% 0.2008 N N ND CGST16 0.8355 0.1744% 0.9974 Y Y 0.93% CGST18 0.9111 0.0299% 0.3842 N N 0.14% CGST21 0.2687 0.2299% 0.9910 Y Y — CGST26 0.9140 0.0399% 0.5009 N N — CG3T28 0.7832 0.1295% 0.9955 Y Y 1.62% CGST30 0.9121 0.0338% 0.9183 Y Y 0.42% CGST32 0.8639 0.0247% 0.9612 Y Y 2.99% CGST33 0.7770 0.0799% 0.9805 Y Y 2.32% CGST38 0.8758 0.0540% 0.9416 Y Y — CGST39 0.9401 0.0287% 0.8480 Y Y ND CGST41 0.9284 0.0398% 0.9263 Y Y ND CGST45 0.9036 0.0220% 0.9713 Y Y ND CGST47 0.9096 0.0157% 0.9687 Y Y 0.45% CGST48 0.5445 0.0220% 0.9975 Y Y 4.21% CGST53 0.7888 0.1140% 0.9914 Y Y CGST58 0.9094 0.0596% 0.9705 Y Y ND CGST67 0.8853 0.3245% 0.9002 Y Y — CGST77 0.8295 0.1851% 0.9981 Y Y — CGST80 0.8846 0.0490% 0.9513 Y Y 1.04% CGST81 0.8851 0.0138% 0.9748 Y Y 0.20% *ND indicates not detected. Please see reference 10 for additional information on targeted sequencing analyes. DELFI cancer detection at 95% and 98% specificity is based on scores greater than 0.6200 and 0.7500, respectively. 

1.-67. (canceled)
 68. A method of determining a cell free DNA (cfDNA) fragmentation profile of a mammal, the method comprising: processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries; subjecting the sequencing libraries to low-coverage whole genome sequencing to obtain sequenced fragments; mapping the sequenced fragments to a genome to obtain windows of mapped sequences; and analyzing the windows of mapped sequences to determine cfDNA fragment lengths.
 69. The method of claim 68, wherein the mapped sequences comprise tens or hundreds to thousands of windows.
 70. The method of claim 68, wherein the windows are non-overlapping windows.
 71. The method of claim 68, wherein the windows each comprises thousands to millions of base pairs.
 72. The method of claim 68, wherein a cfDNA fragmentation profile is determined within each window.
 73. The method of claim 68, wherein cfDNA fragmentation profile comprises a median fragment size.
 74. The method of claim 68, wherein cfDNA fragmentation profile comprises a fragment size distribution.
 75. The method of claim 68, wherein the cfDNA fragmentation profile comprises a ratio of small cfDNA fragments to large cfDNA fragments in said windows of mapped sequences.
 76. The method of claim 68, wherein the cfDNA fragmentation profile comprises the sequence coverage of small cfDNA fragments in windows across the genome.
 77. The method of claim 68, wherein the cfDNA fragmentation profile comprises the sequence coverage of large cfDNA fragments in windows across the genome.
 78. The method of claim 68, wherein the cfDNA fragmentation profile comprises the sequence coverage of small and large cfDNA fragments in windows across the genome.
 79. The method of claim 68, wherein the cfDNA fragmentation profile is over the whole genome or a subgenomic interval.
 80. A method of identifying a mammal as having cancer, the method comprising: determining a cell free DNA (cfDNA) fragmentation profile in a sample obtained from the mammal; and identifying the mammal as having cancer when the cfDNA fragmentation profile obtained from the mammal is different from a reference cfDNA fragmentation profile.
 81. The method of claim 80, wherein the reference cfDNA fragmentation profile is a cfDNA fragmentation profile of a healthy mammal.
 82. The method of claim 80, wherein the reference DNA fragmentation pattern is a reference nucleosome cfDNA fragmentation profile.
 83. The method of claim 80, wherein the cfDNA fragmentation profile comprises a median fragment size, and wherein a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile.
 84. The method of claim 80, wherein the cfDNA fragmentation profile comprises a fragment size distribution, and wherein a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile.
 85. The method of claim 80, wherein the cfDNA fragmentation profile comprises a ratio of small cfDNA fragments to large cfDNA fragments in said windows of mapped sequences, wherein a small cfDNA fragment is about 100 base pairs (bp) to 150 bp in length, wherein a large cfDNA fragments is about 151 bp to 220 bp in length, and wherein a correlation of fragment ratios in the cfDNA fragmentation profile is lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile.
 86. The method of claim 80, wherein the cfDNA fragmentation profile comprises the sequence coverage of large cfDNA fragments in windows across the whole genome or a subgenomic interval.
 87. The method of claim 80, wherein the cfDNA fragmentation profile comprises the sequence coverage of small and/or large cfDNA fragments in windows across the genome or a subgenomic interval.
 88. The method of claim 80, wherein the cancer is selected from the group consisting of: colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, bile duct cancer, and ovarian cancer.
 90. The method of claim 80, wherein identifying a mammal with cancer includes a comparison of the cfDNA fragmentation profile with a reference cfDNA fragmentation profile over a subgenomic interval.
 91. The method of claim 80, wherein the cancer is treated with or has previously been treated with a treatment comprising administering to the mammal a cancer treatment selected from the group consisting of: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, and combinations thereof.
 92. The method of claim 80, further comprising identifying one or more cancer-specific sequence alterations or mutations, one or more chromosomal abnormalities, a copy number change in one or more chromosome arms, or identifying one or more aneuploidies in the sample.
 93. A method of identifying the tissue of origin of a cancer in a mammal identified as having a cancer, the method comprising: determining a cell free DNA (cfDNA) fragmentation profile in a sample obtained from the mammal; and identifying the tissue of origin of the cancer in a mammal when the cfDNA fragmentation profile obtained from the mammal matches a reference cfDNA fragmentation profiles from a mammal identified as having a cancer with the same tissue of origin.
 94. The method of claim 93, wherein the reference cfDNA fragmentation profile comprises reference cfDNA fragmentation profiles from mammals identified as having one or more of colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, bile duct cancer, and ovarian cancer.
 95. The method of claim 93, wherein the reference DNA fragmentation pattern is a reference nucleosome cfDNA fragmentation profile.
 96. The method of claim 68, wherein cfDNA fragmentation patterns provide over 20,000 reads per window.
 97. The method of claim, 68, wherein genome coverage is from 2×, 1×, 0.5×, 0.2× or 0.1×.
 98. A machine learning system for determining a cell free DNA (cfDNA) fragmentation profile of a mammal, the method comprising: processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries; subjecting the sequencing libraries to low-coverage whole genome sequencing to obtain sequenced fragments; mapping the sequenced fragments to a genome to obtain windows of mapped sequences; and analyzing the windows of mapped sequences to determine cfDNA fragment lengths. 